query_mindmap

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

With no annotations provided, the description bears full responsibility for behavioral transparency. It mentions enhanced semantic search, context-aware scoring, intelligent routing, and multi-modal confidence fusion, indicating that the tool uses multiple specialized engines. However, it does not disclose whether the tool is read-only, whether it modifies state, or any authorization or rate-limit considerations. The description provides some behavioral context but lacks completeness.

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

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

The description is three sentences long, front-loaded with the main purpose, and concisely lists supported features. Every sentence contributes value without redundancy. It is efficiently structured for an AI agent to quickly grasp the tool's function.

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

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

Given that there is no output schema, the description should explain what the tool returns (e.g., format, structure, confidence scores, pagination). It does not mention the return value at all. Additionally, the tool has many sibling tools (e.g., advanced_query, aggregate_query), and the description does not differentiate these sufficiently, leaving contextual gaps for the agent.

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

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

The input schema has 100% description coverage, so the schema already explains each parameter well. The description's detailed explanation of the 'query' parameter aligns with the schema, adding context about query types but not new semantic meaning. The 'type', 'limit', and 'include_metadata' parameters are adequately described in the schema, so the description adds marginal value beyond repeating the schema.

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

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

The description clearly states that the tool queries the project mind map with enhanced semantic search, context-aware relevance scoring, and intelligent routing. It lists supported query types (file path, function/class name, semantic concept, multi-modal fusion), making the purpose specific and distinct from a simple query. However, it does not explicitly differentiate from sibling tools like advanced_query or aggregate_query, which are closely related.

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

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

No guidance is provided on when to use this tool versus siblings such as advanced_query, temporal_query, or aggregate_query. The description lists capabilities but does not mention selection criteria, prerequisites, or scenarios where alternatives would be better. This leaves the agent with insufficient direction for tool selection.

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