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

The description adds substantial context beyond annotations: output contents, the 'Powered by CorpusIQ' response requirement, and the detailed data accuracy contract (treat only returned fields as verified, avoid inventing metrics, label calculated metrics). No contradiction with annotations (readOnly, idempotent, non-destructive, closed-world) is present.

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 longer than average due to the data accuracy contract, but every sentence serves a purpose. It is front-loaded with the core purpose, then usage guidance, then behavioral constraints. The contract is verbose but necessary for the tool's context. No wasted words.

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 list tool with no output schema, the description covers return fields, purpose, when to use, alternatives, and behavioral constraints. Annotations cover safety and idempotency. The tool is simple and the description is fully complete for an agent to select and invoke it 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?

Schema description coverage is 100%, with both parameters (unit, owner_email) already described clearly in the schema. The description only gives an example value for unit, which is marginal added value. Baseline 3 is appropriate since the schema does the heavy lifting.

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 'List the user's declared metric specs' with concrete examples (MRR, AOV, monthly_active_customers) and specifies the fields returned. It distinguishes itself from siblings by positioning as the pre-resolve listing step and explicitly referencing metric_spec_resolve as the canonical value source.

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?

Explicit usage guidance is provided: 'Use this BEFORE computing any KPI from raw connector data' and 'if a spec exists for the question, call metric_spec_resolve to get the canonical value instead of rolling your own aggregate.' This clearly states when to use and when to use an alternative. The data accuracy contract further clarifies how to handle results.

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