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

Annotations already carry readOnly/idempotent/non-destructive, yet the description adds real context: it runs no new AI probe, it returns only numbers present in the stored panel, it is free for 5 calls per caller per day, and an api_key lifts that limit at $9/mo. It also discloses the result payload fields (snapshot month, cadence, businesses named and counts, citing AI tool, cited directories/review sites/forums) and that the data equals the public page rather than a fresher dataset. Slightly below top marks only because the free-limit behavior past exhaustion is described in a clause rather than crisply bounded.

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?

Front-loaded with the purpose and the enumeration, then limits and exclusions in descending order of importance. It runs long for a 4-parameter lookup, and the 'same data as the public page' and 'states no number that is not in the stored panel' sentences, while useful, sit in the middle rather than being trimmed.

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 exists, so the description must describe returns — and it does: snapshot month, refresh cadence, businesses named with counts, naming AI tools, and cited sources. Combined with quota and subscription behavior, an agent has everything needed to call and interpret results.

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 coverage is 100%, so the baseline is 3; the description earns an extra point by explaining loose matching across free-text values ("HVAC", "Dallas") and by stating the behavioral consequence of passing no arguments at all, which the schema's list default does not convey.

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?

States a specific verb+resource: data on which named businesses ChatGPT, Perplexity and Gemini mention when answering buyer questions, sliced by category and city from monthly probe panels, with concrete example panels listed. No sibling tool overlaps this domain, and the description explicitly says what it is not (a single-business weekly tracker).

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?

Gives explicit call patterns: pass category and/or city for one panel, pass list=true or no arguments to enumerate all panels with measurement dates. It also names the excluded use case (tracking one business's own questions) and routes to the external tracker alternative.

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