ask_docs

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that the tool returns an 'AI-generated answer with sources' and mentions the user_id parameter for rate limiting. However, it does not disclose authentication requirements, cost implications, or any side effects (e.g., logging), leaving some behavioral aspects ambiguous.

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 and well-structured: it opens with a clear purpose sentence, then lists parameters with brief explanations, and ends with the return value. Each line serves a purpose, and there is no redundant information. This makes it easy for an AI agent to quickly parse the essential 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 tool's moderate complexity (4 parameters, 1 required) and the presence of an output schema, the description is largely sufficient. It covers the main purpose, explains each parameter, and describes the return type. It does not touch on error handling or edge cases, but for a straightforward Q&A tool, this is adequate. Sibling tools are listed, providing context.

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%, so the description must compensate. The docstring briefly explains each parameter: question, context_docs, max_context_length, user_id. While the explanations are minimal, they add meaning beyond the raw schema types. For example, 'context_docs' is described as 'Optional list of document paths to use as context', which clarifies its role. However, more detail on constraints (e.g., valid paths) would improve this.

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's purpose: 'Answer questions about documentation using AI'. It specifies the verb 'answer', the resource 'documentation', and the method 'using AI'. This effectively distinguishes it from sibling tools like search_docs (searching) and analyze_docs (analyzing), making the unique purpose unambiguous.

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 by stating the tool answers questions, but it does not provide explicit guidance on when to use it versus alternatives like search_docs for keyword search or analyze_docs for deeper analysis. No when-not-to-use or exclusion criteria are mentioned, limiting the agent's ability to discriminate between tools.

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