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

Annotations declare readOnlyHint=true and openWorldHint=true, and the description adds meaningful behavioral context: it takes 10-40s, consumes one AI draft, and returns assumptions. It also warns to review assumptions before calling generate_spec. No contradiction with annotations.

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?

Three sentences, all information-dense and front-loaded. The core action and return values come first, then cost/latency, then usage routing. No filler.

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?

For a tool with 2 params, full schema coverage, and no output schema, the description covers the key operational facts: what it does, what it returns, how long it takes, its cost, and when to use it. Nothing critical 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 100%, so the schema already documents both parameters. The description adds context about the description parameter (plain-language project description) and the optional type parameter (force the project type), but doesn't add much beyond the schema. Baseline 3 is appropriate.

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 states a specific verb ('fill the whole questionnaire'), the resource (the server's AI/Claude), and the input (plain-language project description). It also names the return values (project type, answers, assumptions), which distinguishes it from siblings like get_questionnaire.

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?

Explicitly says when to use this tool vs get_questionnaire ('Prefer answering get_questionnaire yourself when you already know the project; use this when you only have a one-line idea') and names the next step (generate_spec). This is clear routing guidance.

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