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

With no annotations supplied, the description carries the full burden and does so: it discloses the billing model, that only data types that answer are charged, that the response reports requests_charged, and that a plan-limit message is returned on rate limiting or quota exhaustion. These are exactly the operational traits an agent needs and none are recoverable from the schema.

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 payoff (what the customer says) before the return shape, then cost, then failure modes — a sensible ordering. It is dense but nearly every clause carries information; the cost sentence is slightly compressed ('boards cost 1 request per 10 rows') given boards are not a parameter in this schema, which adds a small amount of noise.

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?

There is no output schema, so the description must describe return values, and it does so field by field (listing, rating, review count, review text, company reply, average stars, star counts, share negative, share replied). Combined with the cost and rate-limit disclosure, an agent has everything needed to call this correctly and interpret the result.

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 every parameter is already documented, and the description adds little parameter-level detail beyond restating that results are per source and reviews are newest-first. The one useful addition is the warning to verify the matched listing is the intended subject, which is a semantic caveat rather than parameter syntax. Baseline 3 is appropriate when 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?

Opens with a concrete verb-and-resource framing ('What customers say about a brand, app, product or place') and then enumerates exactly what comes back per source: matched listing, all-time rating, review count, newest reviews with stars/text/reply, and an aggregate summary. This is specific enough that an agent can distinguish it from get_context without inspecting either schema.

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 routes usage: 'Use it for brand sentiment and complaints; get_context covers social posts' names the sibling and the condition that selects it. It also states the cost model up front (1 request per data type, boards 1 per 10 rows) so the agent can weigh a call before making it.

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