Initial release: iai-mcp v0.1.0
Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: XNLLLLH <XNLLLLH@users.noreply.github.com>
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Areg Noya
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367
mcp-wrapper/src/tools.ts
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367
mcp-wrapper/src/tools.ts
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// Phase-1 (D-12) + Plan 02-04 (MCP-05/07/08) + Plan 03 (CONN-05/07 + AUTIST-13) tools.
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//
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// Tool shapes are JSON-schema dicts consumable by the MCP SDK's ListTools
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// handler. Descriptions are written for Claude's tool-discovery heuristics
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// (concise, task-oriented, reference the autistic-kernel defaults where they
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// affect behaviour).
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//
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// Plan 02-04 adds 3 user-introspection tools:
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// - curiosity_pending (MCP-07): list pending curiosity questions
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// - schema_list (MCP-08): list induced schemas
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// - events_query (MCP-05): user-visible events audit
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//
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// Plan 03 adds 3 scientific-depth tools:
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// - memory_recall_structural (CONN-05): TEM role->filler structural recall
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// - topology (CONN-07): Ashby sigma diagnostic snapshot
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// - camouflaging_status (AUTIST-13): ecological self-regulation status
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import type { PythonCoreBridge } from "./bridge.js";
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export const TOOL_NAMES = [
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"memory_recall",
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"memory_recall_structural",
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"memory_reinforce",
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"memory_contradict",
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"memory_capture",
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"memory_consolidate",
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"profile_get_set",
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"curiosity_pending",
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"schema_list",
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"events_query",
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"topology",
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"camouflaging_status",
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] as const;
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export type ToolName = (typeof TOOL_NAMES)[number];
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interface ToolSchema {
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name: string;
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description: string;
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inputSchema: Record<string, unknown>;
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}
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export const toolSchemas: Record<ToolName, ToolSchema> = {
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memory_recall: {
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name: "memory_recall",
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description:
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"Recall verbatim memories matching cue. Returns hits + anti_hits.",
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inputSchema: {
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type: "object",
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properties: {
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cue: {
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type: "string",
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description: "Natural-language query to match against stored memories.",
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},
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budget_tokens: {
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type: "integer",
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description: "Soft token budget for response (default 1500).",
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default: 1500,
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},
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session_id: {
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type: "string",
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description:
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"Current session id; gets written into every recalled record's provenance (MEM-05).",
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},
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cue_embedding: {
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type: "array",
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items: { type: "number" },
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description:
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"Optional pre-computed embedding vector for the cue " +
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"(EMBED_DIM=384 floats; bge-small-en-v1.5). " +
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"When omitted, the daemon embeds the cue server-side. " +
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"Used by memory_contradict and tests that need byte-stable embeddings.",
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},
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language: {
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type: "string",
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description:
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"Optional ISO-639-1 language hint for the sleep-suggestion path " +
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"(8 supported: en/ru/ja/ar/de/fr/es/zh). Defaults to 'en' " +
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"when omitted. Hot-path retrieval is language-agnostic; this " +
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"key only affects the sleep-suggestion regex pre-screen.",
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},
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},
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required: ["cue"],
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},
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},
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memory_reinforce: {
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name: "memory_reinforce",
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description:
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"Boost Hebbian edges among co-retrieved record ids.",
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inputSchema: {
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type: "object",
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properties: {
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ids: {
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type: "array",
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items: { type: "string", format: "uuid" },
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description: "Record UUIDs that were co-retrieved in the current context.",
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},
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},
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required: ["ids"],
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},
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},
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memory_contradict: {
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name: "memory_contradict",
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description:
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"Mark a record contradicted; new fact stored as new record.",
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inputSchema: {
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type: "object",
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properties: {
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id: {
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type: "string",
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format: "uuid",
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description: "UUID of the record being contradicted.",
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},
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new_fact: {
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type: "string",
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description: "The updated verbatim fact. Stored as a new record.",
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},
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cue_embedding: {
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type: "array",
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items: { type: "number" },
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description:
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"Optional pre-computed embedding vector for the contradicting " +
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"fact (EMBED_DIM=384 floats; bge-small-en-v1.5). When omitted, " +
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"the daemon embeds new_fact server-side.",
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},
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},
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required: ["id", "new_fact"],
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},
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},
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memory_capture: {
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name: "memory_capture",
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description:
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"Capture a verbatim turn. Auto-dedups at cos>=0.95 (reinforces). " +
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"Use for corrections + load-bearing decisions.",
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inputSchema: {
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type: "object",
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properties: {
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text: {
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type: "string",
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description:
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"Verbatim text to capture (user utterance, Claude decision, or observation). " +
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"Min 12 chars, max 8000 (longer is truncated).",
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},
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cue: {
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type: "string",
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description:
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"Short natural-language cue used for embedding + dedup lookup. " +
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"If empty, `text` itself is embedded.",
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},
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tier: {
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type: "string",
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enum: ["working", "episodic", "semantic", "procedural", "parametric"],
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default: "episodic",
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description:
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"Memory tier. Default 'episodic' (verbatim user utterances). " +
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"Use 'semantic' for induced summaries, 'procedural' for learned behaviour notes.",
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},
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session_id: {
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type: "string",
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description: "Current session id for provenance (MEM-05).",
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},
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role: {
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type: "string",
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enum: ["user", "assistant", "system"],
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default: "user",
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description: "Who produced this turn — tags the record for filtering.",
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},
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},
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required: ["text"],
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},
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},
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memory_consolidate: {
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name: "memory_consolidate",
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description:
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"Trigger memory consolidation.",
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inputSchema: {
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type: "object",
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properties: {
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session_id: {
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type: "string",
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description:
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"Optional session id used for provenance tagging on the " +
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"consolidate event. Defaults to '-' when omitted.",
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},
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},
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},
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},
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profile_get_set: {
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name: "profile_get_set",
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description:
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"Read or write a profile knob (11 sealed: 10 AUTIST + wake_depth). operation: get|set.",
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inputSchema: {
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type: "object",
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properties: {
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operation: {
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type: "string",
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enum: ["get", "set"],
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description: "Whether to read or write a knob.",
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},
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knob: {
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type: "string",
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description: "Knob name. Omit on 'get' to retrieve all live + deferred knobs.",
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},
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value: {
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description: "New value when operation='set'. Any JSON-serialisable type.",
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},
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},
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required: ["operation"],
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},
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},
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curiosity_pending: {
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name: "curiosity_pending",
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description:
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"List pending curiosity questions. Optional session_id filter.",
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inputSchema: {
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type: "object",
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properties: {
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session_id: {
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type: "string",
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description: "Only return questions from this session.",
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},
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},
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},
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},
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schema_list: {
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name: "schema_list",
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description:
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"List induced schemas. Optional domain + confidence_min filters.",
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inputSchema: {
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type: "object",
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properties: {
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domain: {
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type: "string",
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description: "Only return schemas tagged with this domain (e.g. 'coding').",
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},
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confidence_min: {
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type: "number",
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description: "Minimum parsed confidence (0.0-1.0). Default 0.0.",
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default: 0.0,
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},
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},
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},
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},
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events_query: {
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name: "events_query",
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description:
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"Query user-visible events by kind, since, severity, limit.",
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inputSchema: {
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type: "object",
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properties: {
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kind: {
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type: "string",
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description:
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"Event kind. Must be in the whitelist (see tool description).",
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},
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since: {
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type: "string",
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description: "ISO-8601 timestamp; only events at or after this are returned.",
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},
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severity: {
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type: "string",
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enum: ["info", "warning", "critical"],
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description: "Optional severity filter.",
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},
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limit: {
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type: "integer",
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description: "Maximum events returned (default 100, capped at 1000).",
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default: 100,
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},
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},
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required: ["kind"],
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},
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},
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memory_recall_structural: {
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name: "memory_recall_structural",
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description:
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"Structural recall via role-filler bindings (TEM). O(N) scan; max_records caps.",
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inputSchema: {
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type: "object",
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properties: {
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structure_query: {
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type: "object",
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description:
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"Optional role->filler map, e.g. {\"agent\": \"Alice\"}. Each value is hashed to a filler hypervector. When omitted or empty, query HV is zero-filled and every row with structure_hv is scored (expensive at large N).",
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additionalProperties: { type: "string" },
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},
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budget_tokens: {
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type: "integer",
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description: "Soft token budget for response (default 2000).",
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default: 2000,
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},
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max_records: {
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type: "integer",
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description:
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"Hard cap on records scanned after fetch (default 5000, max 50000). Prevents accidental full-corpus scans from `{}`.",
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default: 5000,
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},
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},
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required: [],
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},
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},
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topology: {
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name: "topology",
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description:
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"Topology snapshot: N, C, L, sigma, community_count, regime.",
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inputSchema: { type: "object", properties: {} },
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},
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camouflaging_status: {
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name: "camouflaging_status",
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description:
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"Camouflaging detection status; window_size weekly points.",
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inputSchema: {
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type: "object",
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properties: {
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window_size: {
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type: "integer",
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description: "Weekly points in the sliding window (default 5).",
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default: 5,
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},
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},
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},
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},
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};
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export async function invokeTool(
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bridge: PythonCoreBridge,
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name: ToolName,
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args: Record<string, unknown>,
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): Promise<unknown> {
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switch (name) {
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case "memory_recall":
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return bridge.call("memory_recall", args);
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case "memory_reinforce":
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return bridge.call("memory_reinforce", args);
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case "memory_contradict":
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return bridge.call("memory_contradict", args);
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case "memory_capture":
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return bridge.call("memory_capture", args);
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case "memory_consolidate":
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return bridge.call("memory_consolidate", args);
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case "profile_get_set": {
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const op = args.operation as string;
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if (op === "get") {
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return bridge.call("profile_get", { knob: args.knob ?? null });
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}
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if (op === "set") {
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return bridge.call("profile_set", {
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knob: args.knob,
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value: args.value,
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});
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}
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throw new Error(`unknown operation ${op}`);
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}
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case "curiosity_pending":
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return bridge.call("curiosity_pending", args);
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case "schema_list":
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return bridge.call("schema_list", args);
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case "events_query":
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return bridge.call("events_query", args);
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case "memory_recall_structural":
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return bridge.call("memory_recall_structural", args);
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case "topology":
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return bridge.call("topology", args);
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case "camouflaging_status":
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return bridge.call("camouflaging_status", args);
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}
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}
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