rag_query_data

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

No annotations are provided, so the description must carry the burden. It does not disclose any behavioral traits such as read-only nature, required permissions, performance implications, or behavior on empty results. For a tool with 6 parameters, this is insufficient.

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 a single sentence that directly states the purpose. It is concise but could be improved by adding key behavioral details without becoming verbose.

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 the tool has 6 parameters, no output schema, and no annotations, the description is insufficient. It does not explain return format, modes, or how parameters affect results. The agent would need external knowledge to use this tool effectively.

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?

Schema description coverage is only 17% (only query has a description in schema). The tool description adds no parameter details beyond the schema. It fails to compensate for the low coverage, leaving agents without understanding of mode, top_k, include_references, etc.

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 the tool returns structured LightRAG query data including entities, relations, chunks, and metadata. It uses a specific verb 'Return' and resource 'LightRAG query data', and distinguishes from sibling tools like rag_ask (text answer) and rag_get_context (context only).

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 on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or comparison with siblings like rag_ask or rag_get_context. The agent lacks information to choose appropriately.

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