codebook_query

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

With no annotations, the description bears full burden. It mentions 'geometrically' but does not explain what that entails (e.g., vector embeddings, similarity measure). It also omits whether the tool is read-only, requires network access, or has side effects.

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 concise single sentence (14 words) that front-loads the action and resource. Every word contributes meaning with zero 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?

Given the tool has 4 parameters, an output schema, and no annotations, the description is too minimal. It fails to explain the geometric method, the meaning of 'most related', or how the parameters affect behavior. Important context is missing.

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 0%, yet the description provides no information about any parameters (notes_dir, text, glob, k). The agent receives no help understanding parameter roles or expected inputs 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 the tool finds notes related to a phrase using geometric similarity, explicitly contrasting with keyword and LLM-based methods. This specificity distinguishes it from typical search tools.

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 usage for semantic search but provides no guidance on when to use this tool over siblings like codebook_neighbors or codebook_related. No exclusions or alternatives are mentioned.

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