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

With no annotations, the description carries full burden and it does well: it states the data range (US only), describes the return format (month-end snapshots, missing months not filled, empty list meaning), and provides a detailed new-product definition including fallback logic for launch date. This goes beyond what the schema provides and fully discloses key behaviors.

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 well-structured with numbered sections, front-loading the function purpose and use cases, then providing an example and additional context. It is detailed but not overly verbose; each section adds value. It could be slightly more concise, but the density is reasonable for a complex 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?

Given the tool's complexity (output structure, new-product definition, missing data behavior), the description is comprehensive: it covers return format, data range, example call, and the crucial new-product definition. With no output schema, the description fills the gap by explaining what data.points[] contains. Sibling tools are differentiated, and the agent has everything needed to call 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%, so the schema already documents all six parameters. The description adds an example call that illustrates parameter values but doesn't add extra semantics beyond the schema. It does explain the new-product definition which is crucial for interpreting the data, but that's more about output than parameters. Baseline 3 is appropriate since schema covers everything.

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 the tool returns data.points[] in ascending date order for one US category market, with specific measures under marketTotal and marketSample. It distinguishes it from sibling tools like openapi_v2_markets_search and openapi_v2_markets_structure_profile by explicitly stating they serve different use cases (snapshot and distribution evidence).

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?

The description provides explicit use cases, including when to use history vs. search vs. structure-profile, and guides the agent to use search for current snapshot and structure-profile for distribution evidence. However, it doesn't mention when NOT to use this tool beyond those alternatives, so it's not fully explicit about exclusions.

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