query_knowledge_base

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

With no annotations, the description itself must convey behavior. It explicitly describes the search-and-generate behavior, implying a read-only operation, but omits any details about response format, permissions, or side effects. For a simple query tool, this basic disclosure is minimally adequate.

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 concise, with a front-loaded action sentence and a brief Args note. Every sentence offers useful information; the example contributes to parameter clarity without redundancy.

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?

The description covers the core purpose and parameter semantics, and an output schema exists. However, it does not provide explicit guidance on when to choose this tool over sibling tools, leaving a gap in the overall contextual picture.

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 only provides a 'question' string with no description. The tool description adds meaning by explaining it is the user's question and gives a concrete example ('小明的宠物叫什么？'). This fully compensates for the 0% schema description coverage.

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 finds the most relevant content from a local knowledge base and generates an answer. This is a specific verb+resource+action, and it distinguishes from sibling tools like search_relevant_chunks (which likely just returns chunks without generation) and list_documents.

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?

The description implies use for answering questions from a local knowledge base, but it does not explicitly contrast with search_relevant_chunks or list_documents. No when-to-use versus alternatives guidance is provided, leaving usage context somewhat implicit.

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