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

Annotations already cover safety (readOnly, idempotent, not open-world), so the bar is lower, and the description still adds a meaningful sampling caveat: results are read from at most 200 facts and are indicative rather than exhaustive. It could go further on latency or failure modes, but the sampling disclosure is exactly the kind of behavior an agent needs to interpret results.

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 response contents are front-loaded in the first clause, then usage guidance, then the caveat and the sibling pointer. It is dense but every sentence carries a distinct instruction; it is slightly long for a one-parameter tool.

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?

With no output schema, the description fully carries the burden by enumerating what the response contains (unit, fields, entity types, sources, status shares, example with fact_url) and stating the sampling limit, so an agent knows both how to call it and how to 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?

There is one parameter (`predicate`) with 0% schema description coverage, so the schema gives no semantic help. The description implicitly clarifies the parameter by framing it as a metric name you should not guess, but it adds no format, naming-convention, or matching details.

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+resource (describe a predicate/metric name) and enumerates exactly what it returns: unit, carried fields, entity types, sources, status distribution, and a citable example with fact_url. This is clearly distinguishable from siblings like get_timeseries or get_facts.

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?

It gives explicit timing instructions ("Call this BEFORE guessing a predicate name and before presenting a number whose provenance you cannot state") and names the alternative for a different need ("For the exact coverage of one entity use get_timeseries"). When-to-use, when-not, and the routing alternative are all present.

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