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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds genuine behavioral context beyond that: it discloses that source attributions 'must travel with the data' (a licensing obligation on downstream use) and that a time-limited key carries terms and an end date.

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?

A single dense sentence with the headline content (markets covered, company counts) front-loaded, and the only parameter behavior moved to the end. It is long but nearly every clause names a distinct returned artifact, so little is wasted.

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 carry the return-value burden, and it does so thoroughly, listing each section of the payload plus the licence caveat. The only real gap is not saying how to use the result or how it differs from get_data_dictionary.

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?

With one parameter and 100% schema description coverage, the baseline is 3. The description restates the compact effect, but its phrasing ('per-market notes and accounting descriptions') only loosely matches the schema's 'market_notes, schema', so it adds little meaning beyond what the schema already provides.

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 enumerates exactly what the tool returns: markets covered, company counts, history depth, full_history_sample, metric vocabulary, source attributions, and licence terms. That is far more specific than a tautology, but it is a bare noun list with no verb and it does not distinguish itself from get_data_dictionary, which the mention of 'the metric vocabulary' partially overlaps with.

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?

There is no explicit statement of when to call this versus the siblings. Usage is only implied by the content list (call it to learn what data exists before querying) and by the compact note implying a token-saving call pattern. No exclusions or named alternatives are given.

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