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Cyber MacGeddon
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@ -249,9 +249,26 @@ class McpServer:
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flow_id: str | None = None,
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ctx: Context = None,
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) -> EmbeddingsResponse:
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"""
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Compute text embeddings
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Generate vector embeddings for the given text using TrustGraph's embedding models.
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This tool converts text into high-dimensional vectors that capture semantic meaning,
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enabling similarity searches, clustering, and other vector-based operations.
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Args:
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text: The input text to convert into embeddings. Can be a sentence, paragraph,
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or document. The text will be processed by the configured embedding model.
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flow_id: Optional flow identifier to use for processing (default: "default").
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Different flows may use different embedding models or configurations.
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Returns:
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EmbeddingsResponse containing a list of vectors. Each vector is a list of floats
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representing the text's semantic embedding in the model's vector space.
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Example usage:
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- Convert a query into embeddings for similarity search
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- Generate embeddings for documents before storing them
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- Create embeddings for comparison with existing knowledge
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"""
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logging.info("Embeddings request made")
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@ -291,7 +308,30 @@ class McpServer:
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flow_id: str | None = None,
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ctx: Context = None,
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) -> TextCompletionResponse:
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"""Execute an LLM prompt"""
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"""
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Generate text completions using TrustGraph's language models.
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This tool sends prompts to configured language models and returns generated text.
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It supports both user prompts and system instructions for controlling generation.
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Args:
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prompt: The main prompt or question to send to the language model.
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This is the primary input that guides the model's response.
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system: Optional system prompt that sets the context, role, or behavior
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for the AI assistant (e.g., "You are a helpful coding assistant").
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System prompts influence how the model interprets and responds.
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flow_id: Optional flow identifier (default: "default"). Different flows
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may use different models, parameters, or processing pipelines.
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Returns:
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TextCompletionResponse containing the generated text response from the model.
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Example usage:
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- Ask questions and get AI-generated answers
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- Generate code, documentation, or creative content
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- Perform text analysis, summarization, or transformation tasks
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- Use system prompts to control tone, style, or domain expertise
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"""
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if system is None: system = ""
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if flow_id is None: flow_id = "default"
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@ -336,7 +376,38 @@ class McpServer:
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flow_id: str | None = None,
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ctx: Context = None,
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) -> GraphRagResponse:
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"""Execute a GraphRAG question"""
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"""
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Perform Graph-based Retrieval Augmented Generation (GraphRAG) queries.
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GraphRAG combines knowledge graph traversal with language model generation to provide
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contextually rich answers. It explores relationships between entities to build relevant
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context before generating responses.
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Args:
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question: The question or query to answer using the knowledge graph.
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The system will find relevant entities and relationships to inform the response.
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user: User identifier for access control and personalization (default: "trustgraph").
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collection: Knowledge collection to query (default: "default").
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Different collections may contain domain-specific knowledge.
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entity_limit: Maximum number of entities to retrieve during graph traversal.
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Higher limits provide more context but increase processing time.
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triple_limit: Maximum number of relationship triples to consider.
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Controls the depth of relationship exploration.
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max_subgraph_size: Maximum size of the subgraph to extract for context.
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Larger subgraphs provide richer context but use more resources.
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max_path_length: Maximum path length to traverse in the knowledge graph.
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Longer paths can discover distant but relevant relationships.
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flow_id: Processing flow to use (default: "default").
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Returns:
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GraphRagResponse containing the generated answer informed by knowledge graph context.
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Example usage:
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- Answer complex questions requiring multi-hop reasoning
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- Explore relationships between entities in your knowledge base
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- Generate responses grounded in structured knowledge
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- Perform research queries across connected information
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"""
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if user is None: user = "trustgraph"
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if collection is None: collection = "default"
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@ -386,7 +457,35 @@ class McpServer:
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flow_id: str | None = None,
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ctx: Context = None,
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) -> AgentResponse:
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"""Execute an agent question"""
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"""
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Execute intelligent agent queries with reasoning and tool usage capabilities.
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The agent can perform complex multi-step reasoning, use tools, and provide
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detailed thought processes. It's designed for tasks requiring planning,
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analysis, and iterative problem-solving.
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Args:
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question: The question or task for the agent to solve. Can be complex
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queries requiring multiple steps, analysis, or tool usage.
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user: User identifier for personalization and access control (default: "trustgraph").
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collection: Knowledge collection the agent can access (default: "default").
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Determines what information and tools are available.
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flow_id: Agent workflow to use (default: "default"). Different flows
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may have different capabilities, tools, or reasoning strategies.
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Returns:
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AgentResponse containing the final answer after the agent's reasoning process.
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During execution, you'll see intermediate thoughts and observations.
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Example usage:
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- Solve complex analytical problems requiring multiple steps
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- Perform research tasks across multiple information sources
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- Handle queries that need tool usage and decision-making
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- Get detailed explanations of reasoning processes
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Note: This tool provides real-time updates on the agent's thinking process
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through log messages, so you can follow its reasoning steps.
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"""
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if user is None: user = "trustgraph"
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if collection is None: collection = "default"
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@ -454,8 +553,36 @@ class McpServer:
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ctx: Context = None,
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) -> TriplesQueryResponse:
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"""
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Query knowledge graph triples (subject-predicate-object relationships)
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All parameters are optional - omitted parameters act as wildcards
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Query knowledge graph triples using subject-predicate-object patterns.
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Knowledge graphs store information as triples (subject, predicate, object).
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This tool allows flexible querying by specifying any combination of these
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components, with wildcards for unspecified parts.
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Args:
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s_v: Subject value to match (e.g., "John", "Apple Inc."). Leave None for wildcard.
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s_e: Whether subject should be treated as an entity (True) or literal (False).
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p_v: Predicate/relationship value (e.g., "works_for", "type_of"). Leave None for wildcard.
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p_e: Whether predicate should be treated as an entity (True) or literal (False).
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o_v: Object value to match (e.g., "Engineer", "Company"). Leave None for wildcard.
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o_e: Whether object should be treated as an entity (True) or literal (False).
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limit: Maximum number of triples to return (default: 20).
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flow_id: Processing flow identifier (default: "default").
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Returns:
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TriplesQueryResponse containing matching triples from the knowledge graph.
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Example queries:
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- Find all relationships for an entity: s_v="John", others None
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- Find all instances of a relationship: p_v="works_for", others None
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- Find specific facts: s_v="John", p_v="works_for", o_v=None
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- Explore entity types: p_v="type_of", others None
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Use this for:
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- Exploring knowledge graph structure
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- Finding specific facts or relationships
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- Discovering connections between entities
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- Validating or debugging knowledge content
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"""
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if flow_id is None: flow_id = "default"
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@ -509,7 +636,34 @@ class McpServer:
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ctx: Context = None,
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) -> GraphEmbeddingsQueryResponse:
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"""
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Query graph using embedding vectors
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Find entities in the knowledge graph using vector similarity search.
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This tool performs semantic search by comparing embedding vectors to find
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the most similar entities in the knowledge graph. It's useful for finding
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conceptually related information even when exact text matches don't exist.
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Args:
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vectors: List of embedding vectors to search with. Each vector should be
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a list of floats representing semantic embeddings (typically from
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the embeddings tool). Multiple vectors can be provided for batch queries.
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limit: Maximum number of similar entities to return (default: 20).
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Higher limits provide more results but may include less relevant matches.
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flow_id: Processing flow identifier (default: "default").
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Returns:
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GraphEmbeddingsQueryResponse containing entities ranked by similarity to the
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input vectors, along with similarity scores and entity metadata.
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Example workflow:
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1. Use the 'embeddings' tool to convert text to vectors
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2. Use this tool to find similar entities in the knowledge graph
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3. Explore the returned entities for relevant information
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Use this for:
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- Semantic search across knowledge entities
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- Finding conceptually similar content
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- Discovering related entities without exact keyword matches
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- Building recommendation systems based on entity similarity
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"""
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if flow_id is None: flow_id = "default"
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@ -549,7 +703,20 @@ class McpServer:
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ctx: Context = None,
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) -> ConfigResponse:
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"""
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Retrieves complete configuration
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Retrieve the complete TrustGraph system configuration.
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This tool returns all configuration settings for the TrustGraph system,
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including model configurations, API keys, flow definitions, and system parameters.
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Returns:
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ConfigResponse containing the full configuration as a nested dictionary
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with all system settings, organized by category (e.g., models, flows, storage).
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Use this for:
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- Inspecting current system configuration
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- Debugging configuration issues
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- Understanding available models and settings
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- Auditing system setup and parameters
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"""
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if ctx is None:
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@ -584,8 +751,29 @@ class McpServer:
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ctx: Context = None,
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) -> ConfigGetResponse:
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"""
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Retrieves specific configuration entries
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Keys should be list of dicts with 'type' and 'key' fields
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Retrieve specific configuration values by key.
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This tool allows you to fetch specific configuration settings without
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retrieving the entire configuration. Useful for checking particular
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settings or API keys.
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Args:
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keys: List of configuration keys to retrieve. Each key should be a dict with:
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- 'type': Configuration category (e.g., 'llm', 'embeddings', 'storage')
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- 'key': Specific setting name within that category
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Returns:
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ConfigGetResponse containing the requested configuration values.
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Example keys:
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- {'type': 'llm', 'key': 'openai.model'}
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- {'type': 'embeddings', 'key': 'default.model'}
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- {'type': 'storage', 'key': 'database.url'}
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Use this for:
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- Checking specific model configurations
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- Validating API key settings
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- Inspecting individual system parameters
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"""
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if ctx is None:
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@ -621,8 +809,31 @@ class McpServer:
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ctx: Context = None,
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) -> PutConfigResponse:
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"""
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Updates configuration values
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Values should be list of dicts with 'type', 'key', and 'value' fields
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Update system configuration values.
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This tool allows you to modify TrustGraph system settings, such as
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model parameters, API keys, and system behavior configurations.
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Args:
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values: List of configuration updates. Each update should be a dict with:
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- 'type': Configuration category (e.g., 'llm', 'embeddings')
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- 'key': Specific setting name to update
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- 'value': New value for the setting
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Returns:
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PutConfigResponse confirming the configuration update.
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Example updates:
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- {'type': 'llm', 'key': 'openai.model', 'value': 'gpt-4'}
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- {'type': 'embeddings', 'key': 'batch_size', 'value': '100'}
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Use this for:
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- Switching between different models
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- Updating API credentials
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- Modifying system behavior parameters
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- Configuring processing settings
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Note: Configuration changes may require system restart to take effect.
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"""
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if ctx is None:
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@ -655,8 +866,27 @@ class McpServer:
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ctx: Context = None,
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) -> DeleteConfigResponse:
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"""
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Deletes configuration entries
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Keys should be list of dicts with 'type' and 'key' fields
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Delete specific configuration entries from the system.
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This tool removes configuration settings, reverting them to system defaults
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or disabling specific features.
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Args:
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keys: List of configuration keys to delete. Each key should be a dict with:
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- 'type': Configuration category (e.g., 'llm', 'embeddings')
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- 'key': Specific setting name to remove
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Returns:
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DeleteConfigResponse confirming the deletion.
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Use this for:
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- Removing custom model configurations
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- Clearing API credentials
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- Resetting settings to defaults
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- Cleaning up obsolete configurations
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Warning: Deleting essential configuration may cause system functionality
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to be disabled until properly reconfigured.
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"""
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if ctx is None:
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@ -688,7 +918,20 @@ class McpServer:
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ctx: Context = None,
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) -> GetPromptsResponse:
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"""
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Retrieves available prompt templates
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List all available prompt templates in the system.
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Prompt templates are reusable prompts that can be used with language models
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for consistent behavior across different queries and use cases.
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Returns:
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GetPromptsResponse containing a list of available prompt template IDs.
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Each ID can be used with get_prompt to retrieve the full template.
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Use this for:
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- Discovering available prompt templates
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- Exploring pre-configured prompts for different tasks
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- Finding templates for specific use cases
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- Understanding what prompt options are available
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"""
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if ctx is None:
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@ -725,7 +968,24 @@ class McpServer:
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ctx: Context = None,
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) -> GetPromptResponse:
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"""
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Retrieves a specific prompt template
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Retrieve a specific prompt template by ID.
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Prompt templates contain structured prompts with placeholders, instructions,
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and metadata for specific tasks or domains.
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Args:
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prompt_id: The unique identifier of the prompt template to retrieve.
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Use get_prompts to see available template IDs.
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Returns:
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GetPromptResponse containing the complete prompt template with its
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structure, placeholders, and usage instructions.
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Use this for:
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- Examining prompt template structure
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- Understanding how to use specific templates
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- Copying or modifying existing prompts
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- Learning prompt engineering patterns
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"""
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if ctx is None:
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@ -762,7 +1022,19 @@ class McpServer:
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ctx: Context = None,
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) -> GetSystemPromptResponse:
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"""
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Retrieves system prompt configuration
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Retrieve the current system prompt configuration.
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The system prompt defines the default behavior, personality, and instructions
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for language models across the TrustGraph system.
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Returns:
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GetSystemPromptResponse containing the system prompt text and configuration.
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Use this for:
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- Understanding default AI behavior settings
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- Checking current system-wide prompt configuration
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- Auditing AI personality and instruction settings
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- Debugging unexpected AI responses
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"""
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if ctx is None:
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@ -798,7 +1070,22 @@ class McpServer:
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ctx: Context = None,
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) -> ConfigTokenCostsResponse:
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"""
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Retrieves token cost information for different AI models
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Retrieve token pricing information for all configured AI models.
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This tool provides cost information for input and output tokens across
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different language models, helping with budget planning and cost optimization.
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Returns:
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ConfigTokenCostsResponse containing pricing data for each model including:
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- Model name/identifier
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||||
- Input token cost (per token)
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- Output token cost (per token)
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Use this for:
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||||
- Estimating costs for different models
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- Choosing cost-effective models for tasks
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||||
- Budget planning and cost analysis
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||||
- Monitoring and optimizing AI spending
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||||
"""
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if ctx is None:
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@ -846,7 +1133,24 @@ class McpServer:
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ctx: Context = None,
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) -> KnowledgeCoresResponse:
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"""
|
||||
Retrieves list of available knowledge graph cores
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||||
List all available knowledge graph cores for a user.
|
||||
|
||||
Knowledge cores are packaged collections of structured knowledge that can
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be loaded into the system for querying and reasoning. They contain entities,
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relationships, and facts organized as knowledge graphs.
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|
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Args:
|
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user: User identifier to list cores for (default: "trustgraph").
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||||
Different users may have access to different knowledge cores.
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||||
|
||||
Returns:
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KnowledgeCoresResponse containing a list of available knowledge core IDs.
|
||||
|
||||
Use this for:
|
||||
- Discovering available knowledge collections
|
||||
- Understanding what knowledge domains are accessible
|
||||
- Planning which cores to load for specific tasks
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||||
- Managing knowledge resources
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||||
"""
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||||
|
||||
if user is None: user = "trustgraph"
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@ -885,7 +1189,26 @@ class McpServer:
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ctx: Context = None,
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||||
) -> DeleteKgCoreResponse:
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||||
"""
|
||||
Deletes a knowledge graph core
|
||||
Permanently delete a knowledge graph core.
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||||
|
||||
This operation removes a knowledge core from storage. Use with caution
|
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as this action cannot be undone.
|
||||
|
||||
Args:
|
||||
core_id: Unique identifier of the knowledge core to delete.
|
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user: User identifier (default: "trustgraph"). Only cores owned
|
||||
by this user can be deleted.
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||||
|
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Returns:
|
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DeleteKgCoreResponse confirming the deletion.
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||||
|
||||
Use this for:
|
||||
- Cleaning up obsolete knowledge cores
|
||||
- Removing test or experimental data
|
||||
- Managing storage space
|
||||
- Maintaining organized knowledge collections
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||||
|
||||
Warning: This permanently deletes the knowledge core and all its data.
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||||
"""
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||||
|
||||
if user is None: user = "trustgraph"
|
||||
|
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@ -926,7 +1249,27 @@ class McpServer:
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ctx: Context = None,
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||||
) -> LoadKgCoreResponse:
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||||
"""
|
||||
Loads a knowledge graph core
|
||||
Load a knowledge graph core into the active system for querying.
|
||||
|
||||
This operation makes a knowledge core available for GraphRAG queries,
|
||||
triple searches, and other knowledge-based operations.
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||||
|
||||
Args:
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||||
core_id: Unique identifier of the knowledge core to load.
|
||||
flow: Processing flow to use for loading the core. Different flows
|
||||
may apply different processing, indexing, or optimization steps.
|
||||
user: User identifier (default: "trustgraph").
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collection: Target collection name (default: "default"). The loaded
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||||
knowledge will be available under this collection name.
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||||
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||||
Returns:
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||||
LoadKgCoreResponse confirming the core has been loaded.
|
||||
|
||||
Use this for:
|
||||
- Making knowledge cores available for queries
|
||||
- Switching between different knowledge domains
|
||||
- Loading domain-specific knowledge for tasks
|
||||
- Preparing knowledge for GraphRAG operations
|
||||
"""
|
||||
|
||||
if user is None: user = "trustgraph"
|
||||
|
|
@ -968,8 +1311,28 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> GetKgCoreResponse:
|
||||
"""
|
||||
Retrieves a knowledge graph core with streaming data
|
||||
Returns all chunks as a list
|
||||
Download and retrieve the complete content of a knowledge graph core.
|
||||
|
||||
This tool streams the entire content of a knowledge core, returning all
|
||||
entities, relationships, and metadata. Due to potentially large data sizes,
|
||||
the content is streamed in chunks.
|
||||
|
||||
Args:
|
||||
core_id: Unique identifier of the knowledge core to retrieve.
|
||||
user: User identifier (default: "trustgraph").
|
||||
|
||||
Returns:
|
||||
GetKgCoreResponse containing all chunks of the knowledge core data.
|
||||
Each chunk contains part of the knowledge graph structure.
|
||||
|
||||
Use this for:
|
||||
- Examining knowledge core content and structure
|
||||
- Debugging knowledge graph data
|
||||
- Exporting knowledge for backup or analysis
|
||||
- Understanding the scope and quality of knowledge
|
||||
|
||||
Note: Large knowledge cores may take significant time to download.
|
||||
Progress updates are provided through log messages during streaming.
|
||||
"""
|
||||
|
||||
if user is None: user = "trustgraph"
|
||||
|
|
@ -1024,7 +1387,20 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> FlowsResponse:
|
||||
"""
|
||||
Retrieves list of available flows
|
||||
List all available processing flows in the system.
|
||||
|
||||
Flows define processing pipelines for different types of operations
|
||||
(e.g., document processing, knowledge extraction, query handling).
|
||||
Each flow encapsulates a specific workflow with configured steps.
|
||||
|
||||
Returns:
|
||||
FlowsResponse containing a list of available flow identifiers.
|
||||
|
||||
Use this for:
|
||||
- Discovering available processing workflows
|
||||
- Understanding what processing options are available
|
||||
- Choosing appropriate flows for specific tasks
|
||||
- Planning workflow-based operations
|
||||
"""
|
||||
|
||||
if ctx is None:
|
||||
|
|
@ -1059,7 +1435,26 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> FlowResponse:
|
||||
"""
|
||||
Retrieves definition of a specific flow
|
||||
Retrieve the complete definition of a specific processing flow.
|
||||
|
||||
This tool returns the detailed configuration, steps, and parameters
|
||||
of a processing flow, showing how it processes data and what operations it performs.
|
||||
|
||||
Args:
|
||||
flow_id: Unique identifier of the flow to retrieve.
|
||||
|
||||
Returns:
|
||||
FlowResponse containing the complete flow definition including:
|
||||
- Flow configuration and parameters
|
||||
- Processing steps and their order
|
||||
- Input/output specifications
|
||||
- Dependencies and requirements
|
||||
|
||||
Use this for:
|
||||
- Understanding how specific flows work
|
||||
- Debugging flow processing issues
|
||||
- Learning flow configuration patterns
|
||||
- Customizing or duplicating flows
|
||||
"""
|
||||
|
||||
if ctx is None:
|
||||
|
|
@ -1096,7 +1491,20 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> FlowClassesResponse:
|
||||
"""
|
||||
Retrieves list of available flow classes (templates)
|
||||
List all available flow class templates.
|
||||
|
||||
Flow classes are templates that define types of processing workflows.
|
||||
They serve as blueprints for creating specific flow instances with
|
||||
customized parameters.
|
||||
|
||||
Returns:
|
||||
FlowClassesResponse containing a list of available flow class names.
|
||||
|
||||
Use this for:
|
||||
- Discovering available flow templates
|
||||
- Understanding what types of processing are supported
|
||||
- Planning new flow creation
|
||||
- Exploring system capabilities
|
||||
"""
|
||||
|
||||
if ctx is None:
|
||||
|
|
@ -1131,7 +1539,26 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> FlowClassResponse:
|
||||
"""
|
||||
Retrieves definition of a specific flow class
|
||||
Retrieve the definition of a specific flow class template.
|
||||
|
||||
Flow classes define the structure, parameters, and capabilities of
|
||||
flow types. This tool returns the class specification including
|
||||
configurable parameters and processing logic.
|
||||
|
||||
Args:
|
||||
class_name: Name of the flow class to retrieve.
|
||||
|
||||
Returns:
|
||||
FlowClassResponse containing the flow class definition with:
|
||||
- Class parameters and configuration options
|
||||
- Processing capabilities and requirements
|
||||
- Usage instructions and examples
|
||||
|
||||
Use this for:
|
||||
- Understanding flow class capabilities
|
||||
- Learning how to configure new flows
|
||||
- Troubleshooting flow creation issues
|
||||
- Exploring advanced flow features
|
||||
"""
|
||||
|
||||
if ctx is None:
|
||||
|
|
@ -1171,7 +1598,25 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> StartFlowResponse:
|
||||
"""
|
||||
Starts a new flow instance
|
||||
Create and start a new processing flow instance.
|
||||
|
||||
This tool creates a new flow based on a flow class template and starts
|
||||
it running. The flow will begin processing according to its configuration.
|
||||
|
||||
Args:
|
||||
flow_id: Unique identifier for the new flow instance.
|
||||
class_name: Flow class template to use for creating the flow.
|
||||
Use get_flow_classes to see available classes.
|
||||
description: Human-readable description of the flow's purpose.
|
||||
|
||||
Returns:
|
||||
StartFlowResponse confirming the flow has been started.
|
||||
|
||||
Use this for:
|
||||
- Creating new processing workflows
|
||||
- Starting automated processing tasks
|
||||
- Launching background operations
|
||||
- Initiating data processing pipelines
|
||||
"""
|
||||
|
||||
if ctx is None:
|
||||
|
|
@ -1208,7 +1653,22 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> StopFlowResponse:
|
||||
"""
|
||||
Stops a running flow instance
|
||||
Stop a running flow instance.
|
||||
|
||||
This tool gracefully stops a running flow, allowing it to complete
|
||||
current operations before shutting down.
|
||||
|
||||
Args:
|
||||
flow_id: Unique identifier of the flow instance to stop.
|
||||
|
||||
Returns:
|
||||
StopFlowResponse confirming the flow has been stopped.
|
||||
|
||||
Use this for:
|
||||
- Stopping unwanted or completed flows
|
||||
- Managing system resources
|
||||
- Interrupting long-running processes
|
||||
- Maintaining flow lifecycle
|
||||
"""
|
||||
|
||||
if ctx is None:
|
||||
|
|
@ -1243,7 +1703,28 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> DocumentsResponse:
|
||||
"""
|
||||
Retrieves list of all documents in the system
|
||||
List all documents stored in the TrustGraph document library.
|
||||
|
||||
This tool returns metadata for all documents that have been uploaded
|
||||
to the system, including their processing status and properties.
|
||||
|
||||
Args:
|
||||
user: User identifier to list documents for (default: "trustgraph").
|
||||
Only documents owned by this user will be returned.
|
||||
|
||||
Returns:
|
||||
DocumentsResponse containing metadata for each document including:
|
||||
- Document ID and title
|
||||
- Upload timestamp and user
|
||||
- MIME type and size information
|
||||
- Tags and custom metadata
|
||||
- Processing status
|
||||
|
||||
Use this for:
|
||||
- Browsing available documents
|
||||
- Managing document collections
|
||||
- Finding documents by metadata
|
||||
- Auditing document storage
|
||||
"""
|
||||
|
||||
if user is None: user = "trustgraph"
|
||||
|
|
@ -1281,7 +1762,27 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> ProcessingResponse:
|
||||
"""
|
||||
Retrieves list of documents currently being processed
|
||||
List all documents currently in the processing queue.
|
||||
|
||||
This tool shows documents that are being processed or waiting to be
|
||||
processed, along with their processing status and configuration.
|
||||
|
||||
Args:
|
||||
user: User identifier (default: "trustgraph"). Only processing
|
||||
jobs for this user will be returned.
|
||||
|
||||
Returns:
|
||||
ProcessingResponse containing processing metadata including:
|
||||
- Processing job ID and document ID
|
||||
- Processing flow and status
|
||||
- Target collection and user
|
||||
- Timestamp and progress information
|
||||
|
||||
Use this for:
|
||||
- Monitoring document processing progress
|
||||
- Debugging processing issues
|
||||
- Managing processing queues
|
||||
- Understanding system workload
|
||||
"""
|
||||
|
||||
if user is None: user = "trustgraph"
|
||||
|
|
@ -1326,7 +1827,31 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> LoadDocumentResponse:
|
||||
"""
|
||||
Uploads a document to the library with full metadata
|
||||
Upload a document to the TrustGraph document library.
|
||||
|
||||
This tool stores documents with rich metadata for later processing,
|
||||
search, and knowledge extraction. Documents can be text files, PDFs,
|
||||
or other supported formats.
|
||||
|
||||
Args:
|
||||
document: The document content as a string. For binary files,
|
||||
this should be base64-encoded content.
|
||||
document_id: Optional unique identifier. If not provided, one will be generated.
|
||||
metadata: Optional list of custom metadata key-value pairs.
|
||||
mime_type: MIME type of the document (e.g., 'text/plain', 'application/pdf').
|
||||
title: Human-readable title for the document.
|
||||
comments: Optional description or notes about the document.
|
||||
tags: List of tags for categorizing and finding the document.
|
||||
user: User identifier (default: "trustgraph").
|
||||
|
||||
Returns:
|
||||
LoadDocumentResponse confirming the document has been stored.
|
||||
|
||||
Use this for:
|
||||
- Adding new documents to the knowledge base
|
||||
- Storing reference materials and data sources
|
||||
- Building document collections for processing
|
||||
- Importing external content for analysis
|
||||
"""
|
||||
|
||||
if user is None: user = "trustgraph"
|
||||
|
|
@ -1378,7 +1903,26 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> RemoveDocumentResponse:
|
||||
"""
|
||||
Removes a document from the library
|
||||
Permanently remove a document from the library.
|
||||
|
||||
This operation deletes a document and all its associated metadata.
|
||||
Use with caution as this action cannot be undone.
|
||||
|
||||
Args:
|
||||
document_id: Unique identifier of the document to remove.
|
||||
user: User identifier (default: "trustgraph"). Only documents
|
||||
owned by this user can be removed.
|
||||
|
||||
Returns:
|
||||
RemoveDocumentResponse confirming the document has been deleted.
|
||||
|
||||
Use this for:
|
||||
- Cleaning up obsolete or incorrect documents
|
||||
- Managing storage space
|
||||
- Removing sensitive or inappropriate content
|
||||
- Maintaining organized document collections
|
||||
|
||||
Warning: This permanently deletes the document and all its metadata.
|
||||
"""
|
||||
|
||||
if user is None: user = "trustgraph"
|
||||
|
|
@ -1421,7 +1965,33 @@ class McpServer:
|
|||
ctx: Context = None,
|
||||
) -> AddProcessingResponse:
|
||||
"""
|
||||
Adds a document to the processing queue
|
||||
Queue a document for processing through a specific workflow.
|
||||
|
||||
This tool adds a document to the processing queue where it will be
|
||||
processed by the specified flow to extract knowledge, create embeddings,
|
||||
or perform other analysis operations.
|
||||
|
||||
Args:
|
||||
processing_id: Unique identifier for this processing job.
|
||||
document_id: ID of the document to process (must exist in library).
|
||||
flow: Processing flow to use. Different flows perform different
|
||||
types of analysis (e.g., knowledge extraction, summarization).
|
||||
user: User identifier (default: "trustgraph").
|
||||
collection: Target collection for processed knowledge (default: "default").
|
||||
Results will be stored under this collection name.
|
||||
tags: Optional tags for categorizing this processing job.
|
||||
|
||||
Returns:
|
||||
AddProcessingResponse confirming the document has been queued.
|
||||
|
||||
Use this for:
|
||||
- Processing uploaded documents into knowledge
|
||||
- Extracting entities and relationships from text
|
||||
- Creating searchable embeddings
|
||||
- Converting documents into structured knowledge
|
||||
|
||||
Note: Processing may take time depending on document size and flow complexity.
|
||||
Use get_processing to monitor progress.
|
||||
"""
|
||||
|
||||
if user is None: user = "trustgraph"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue