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Updated CLI invocation and config model for tools and mcp (#438)

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* Updated CLI invocation and config model for tools and mcp

* CLI anomalies

* Tweaked the MCP tool implementation for new model

* Update agent implementation to match the new model

* Fix agent tools, now all tested

* Fixed integration tests

* Fix MCP delete tool params
This commit is contained in:
cybermaggedon 2025-07-16 23:09:32 +01:00 • committed by GitHub
parent a96d02da5d
commit 81c7c1181b
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11 changed files with 270 additions and 183 deletions
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4
trustgraph-flow/trustgraph/agent/mcp_tool/service.py
View file

@ -47,8 +47,8 @@ class Service(ToolService):
url = self.mcp_services[name]["url"]
if "name" in self.mcp_services[name]:
remote_name = self.mcp_services[name]["name"]
if "remote-name" in self.mcp_services[name]:
remote_name = self.mcp_services[name]["remote-name"]
else:
remote_name = name

2
trustgraph-flow/trustgraph/agent/react/agent_manager.py
View file

@ -39,7 +39,7 @@ class AgentManager:
"type": arg.type,
"description": arg.description
}
for arg in tool.arguments.values()
for arg in tool.arguments
]
}
for tool in self.tools.values()

110
trustgraph-flow/trustgraph/agent/react/service.py
View file

@ -12,7 +12,7 @@ from ... base import GraphRagClientSpec, ToolClientSpec
from ... schema import AgentRequest, AgentResponse, AgentStep, Error
from . tools import KnowledgeQueryImpl, TextCompletionImpl, McpToolImpl
from . tools import KnowledgeQueryImpl, TextCompletionImpl, McpToolImpl, PromptImpl
from . agent_manager import AgentManager
from . types import Final, Action, Tool, Argument
@ -79,64 +79,76 @@ class Processor(AgentService):
print("Loading configuration version", version)
if self.config_key not in config:
print(f"No key {self.config_key} in config", flush=True)
return
config = config[self.config_key]
try:
# This is some extra stuff to put in the prompt
additional = config.get("additional-context", None)
ix = json.loads(config["tool-index"])
tools = {}
for k in ix:
pc = config[f"tool.{k}"]
data = json.loads(pc)
arguments = {
v.get("name"): Argument(
name = v.get("name"),
type = v.get("type"),
description = v.get("description")
# Load tool configurations from the new location
if "tool" in config:
for tool_id, tool_value in config["tool"].items():
data = json.loads(tool_value)
impl_id = data.get("type")
name = data.get("name")
# Create the appropriate implementation
if impl_id == "knowledge-query":
impl = functools.partial(
KnowledgeQueryImpl,
collection=data.get("collection")
)
arguments = KnowledgeQueryImpl.get_arguments()
elif impl_id == "text-completion":
impl = TextCompletionImpl
arguments = TextCompletionImpl.get_arguments()
elif impl_id == "mcp-tool":
impl = functools.partial(
McpToolImpl,
mcp_tool_id=data.get("mcp-tool")
)
arguments = McpToolImpl.get_arguments()
elif impl_id == "prompt":
# For prompt tools, arguments come from config
config_args = data.get("arguments", [])
arguments = [
Argument(
name=arg.get("name"),
type=arg.get("type"),
description=arg.get("description")
)
for arg in config_args
]
impl = functools.partial(
PromptImpl,
template_id=data.get("template"),
arguments=arguments
)
else:
raise RuntimeError(
f"Tool type {impl_id} not known"
)
tools[name] = Tool(
name=name,
description=data.get("description"),
implementation=impl,
config=data, # Store full config for reference
arguments=arguments,
)
for v in data["arguments"]
}
impl_id = data.get("type")
name = data.get("name")
if impl_id == "knowledge-query":
impl = KnowledgeQueryImpl
elif impl_id == "text-completion":
impl = TextCompletionImpl
elif impl_id == "mcp-tool":
impl = functools.partial(McpToolImpl, name=k)
else:
raise RuntimeError(
f"Tool-kind {impl_id} not known"
)
tools[data.get("name")] = Tool(
name = name,
description = data.get("description"),
implementation = impl,
config=data.get("config", {}),
arguments = arguments,
)
# Load additional context from agent config if it exists
additional = None
if self.config_key in config:
agent_config = config[self.config_key]
additional = agent_config.get("additional-context", None)
self.agent = AgentManager(
tools=tools,
additional_context=additional
)
print("Prompt configuration reloaded.", flush=True)
print(f"Loaded {len(tools)} tools", flush=True)
print("Tool configuration reloaded.", flush=True)
except Exception as e:

62
trustgraph-flow/trustgraph/agent/react/tools.py
View file

@ -1,11 +1,24 @@
import json
from .types import Argument
# This tool implementation knows how to put a question to the graph RAG
# service
class KnowledgeQueryImpl:
def __init__(self, context):
def __init__(self, context, collection=None):
self.context = context
self.collection = collection
@staticmethod
def get_arguments():
return [
Argument(
name="question",
type="string",
description="The question to ask the knowledge base"
)
]
async def invoke(self, **arguments):
client = self.context("graph-rag-request")
print("Graph RAG question...", flush=True)
@ -18,6 +31,17 @@ class KnowledgeQueryImpl:
class TextCompletionImpl:
def __init__(self, context):
self.context = context
@staticmethod
def get_arguments():
return [
Argument(
name="question",
type="string",
description="The text prompt or question for completion"
)
]
async def invoke(self, **arguments):
client = self.context("prompt-request")
print("Prompt question...", flush=True)
@ -29,18 +53,24 @@ class TextCompletionImpl:
# the mcp-tool service.
class McpToolImpl:
def __init__(self, context, name):
def __init__(self, context, mcp_tool_id):
self.context = context
self.name = name
self.mcp_tool_id = mcp_tool_id
@staticmethod
def get_arguments():
# MCP tools define their own arguments dynamically
# For now, we return empty list and let the MCP service handle validation
return []
async def invoke(self, **arguments):
client = self.context("mcp-tool-request")
print(f"MCP tool invocation: {self.name}...", flush=True)
print(f"MCP tool invocation: {self.mcp_tool_id}...", flush=True)
output = await client.invoke(
name = self.name,
parameters = {},
name = self.mcp_tool_id,
parameters = arguments, # Pass the actual arguments
)
print(output)
@ -50,4 +80,22 @@ class McpToolImpl:
else:
return json.dumps(output)
# This tool implementation knows how to execute prompt templates
class PromptImpl:
def __init__(self, context, template_id, arguments=None):
self.context = context
self.template_id = template_id
self.arguments = arguments or [] # These come from config
def get_arguments(self):
# For prompt tools, arguments are defined in configuration
return self.arguments
async def invoke(self, **arguments):
client = self.context("prompt-request")
print(f"Prompt template invocation: {self.template_id}...", flush=True)
return await client.prompt(
id=self.template_id,
variables=arguments
)