Get Harpd research reports and the AI market index

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

Beyond the readOnlyHint/openWorldHint annotations, the description supplies rich interpretive context: pointsShare is a share of promotional rankPoints bought with Credits, explicitly NOT an editorial quality or market-merit score. It also discloses data-availability constraints (reports exist only for closed months with enough ranked products) and asserts full provenance on every record, which materially prevents misuse.

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 front-loaded with the resource scope and then delivers two high-value caveats (pointsShare semantics and closed-month availability) that prevent real misinterpretation. It is slightly longer than strictly necessary - the quality-score point is restated twice ('NOT an editorial quality score' and 'not a share of quality or of market merit') - which keeps it from a perfect score.

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 carries the burden of explaining return values, and it does so at a useful level: record counts, row semantics, the meaning of share fields, and availability constraints. Combined with the readOnly/openWorld annotations and fully documented parameters, an agent has what it needs to call this correctly; only per-field detail of the report records is left unspecified.

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 input schema already documents all six parameters, and the description adds no parameter-specific guidance beyond it. The 'IMPORTANT' caveat clarifies the semantic meaning of an output field (pointsShare) rather than any input parameter, so the description correctly holds at the high-coverage baseline of 3.

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 opens with a specific verb and resource ('Read Harpd research output') and enumerates exactly what that includes: 5 monthly report records and a 27-row AI Market Index with product counts, product share, and rank-point share. This concrete scope distinguishes it from the product/ranking sibling tools (get_rankings, get_product, get_category_ranking) without needing to inspect any 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?

The description makes the tool's domain clear ('research output... published monthly research reports... market index'), so an agent can infer it is for Harpd's published analysis rather than raw ranking or product data. However, it never names an alternative sibling (e.g., get_rankings or get_evidence) or states when not to use this tool, leaving the guidance implied rather than explicit.

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