Get Harpd evidence claims for a dataset or product

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

Beyond the annotations (readOnlyHint, openWorldHint), the description discloses meaningful behaviors: it returns claims that fail the evidence chain with their violations rather than hiding them, includes claims Harpd explicitly does not make, and records that rankPoints are promotional placement bought with Credits, not an editorial quality score. This adds rich context beyond the structured annotations.

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 core purpose, followed by usage guidance and important behavioral caveats. Every sentence earns its place: purpose, use case, rankPoints clarification, violation handling, and provenance. The length is justified by the tool's complexity and the need to prevent misuse.

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?

Despite having no output schema, the description explains what is returned (evidence, dataset, methodology, source, timestamp, provenance, violations, non-claims) and clarifies critical domain facts. Combined with the comprehensive input schema and annotations, an agent has enough context to invoke this tool correctly and interpret results.

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 parameters are already fully documented. The description does not add parameter-specific semantics (e.g., how query or productName interact with the evidence graph), but it does add domain context like the rankPoints caveat. Baseline 3 is appropriate because the schema carries the parameter 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?

States a specific action ('Query the Harpd Evidence Graph') with a clear resource and scope: every published claim resolved to evidence, dataset, methodology, source, and timestamp, including claims not made. This distinguishes it from sibling tools like get_product or get_rankings, which focus on products or rankings rather than evidence-backed claims.

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?

Gives clear context on when to use the tool: 'Use this to check whether a statement about Harpd data is actually supported before repeating it.' It does not explicitly name alternatives or exclusion conditions, but the usage scenario is well defined and implies this is the evidence-verification tool among the siblings.

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