stats

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

No annotations, so description carries burden. It clearly states this is a read-style 'Return' operation with no side effects implied meetabst, and it specifies what data is included (decision_rate and reason_breakdown). It doesn't explicitly state it's non-destructive or need auth, but the context implies a read-only stats query. No contradictions.

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?

Two sentences, front-loaded with the purpose and two key outputs. Efficient.

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?

No output schema, so description explains the two things returnedches. It references spec sections, which is helpful. It's sufficient for the tool's simple scope, though it doesn't detail the exact breakdown structure. Minor gap.

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?

No parameters exist threre, so this dimension is trivially satisfied. The description explains what the output contains (decision_rate, reason_breakdown) which helps the agent understand what the tool will produce, but doesn't add parameter meaning since there are none.

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?

Clear verb+resource: 'Return aggregate stats', specifies two concrete outputs (decision_rate and reason_breakdown) with explicit formulas, and references project specs. It distinguishes from the sole sibling 'verify' by being stats-focused, not per-item verification.

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?

Justifies use by linking decision_rate to 'sole metric of project success' and reason_breakdown to 'drives what the next sprint implements,' giving clear contexts for calling. It doesn't explicitly say when not to use or contrast with the sibling 'verify', but the purpose is specific enough that an agent can decide.

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