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

The description discloses that the tool uses pre-computed embeddings and makes no LLM call, which is helpful. However, without annotations, it does not explicitly state that the operation is read-only or mention side effects, though inference suggests it's safe.

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 four sentences, each adding value: purpose, top_k default, scope explanation, return type, and nature (no LLM). No redundant or unnecessary information.

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 straightforward nature of the tool and the presence of an output schema, the description covers key aspects: purpose, parameter defaults, scope options, and operational nature. It is sufficiently complete for an agent to use correctly.

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 0% description coverage, but the description fully compensates by explaining the behavior of each parameter: top_k defaults to a config field, scope options ('raw', 'wiki', 'both') are enumerated, and the purpose of query is implied. This adds significant meaning beyond 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 'Search the knowledge base for relevant document chunks,' indicating a specific verb and resource. There are no sibling tools with similar purpose, so no need for differentiation.

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 provides context on top_k defaulting to config and scope options, but lacks explicit guidance on when to use this tool versus alternatives or when not to use it. It implies usage for semantic search but does not contrast with potential exact-match tools.

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