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Cognitive Exoskeleton MCP Server

Cognitive Exoskeleton MCP Server

by hanjiang-215
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query_mind

Answer questions by retrieving relevant entities and relationships from your personal knowledge graph, then using LLM reasoning to derive insights. Supports shallow and deep retrieval modes.

Instructions

Answer a question using your personal knowledge graph. Retrieves relevant entities and relationships, then uses the LLM to reason over them. Supports shallow (1-hop) and deep (2-hop) retrieval modes.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "question": "<question>"
    },
    "name": "query_mind"
  }
}

Input Schema

NameRequiredDescriptionDefault
depthNoRetrieval depth: 'shallow' (1-hop, default) or 'deep' (2-hop with path reasoning)
questionYesThe question to answer from your knowledge graph

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses the internal process: retrieving relevant entities/relationships, reasoning with an LLM, and supporting two depth modes. It does not mention side effects, but the query nature implies read-only behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences, front-loaded with the main purpose, followed by supporting details. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter tool, the description adequately covers purpose, mechanism, and modes. It lacks an explicit description of the return value, but 'Answer a question' implies a textual answer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers both parameters with descriptions, reaching 100% coverage. The description repeats the depth definition but adds no additional semantic value beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: answering a question using a personal knowledge graph, with retrieval and reasoning. It distinguishes from sibling tools by focusing on question-answering and explicit depth modes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for question-answering over the knowledge graph, but it does not explicitly compare to alternatives like recall_context or discover_connections, nor does it state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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