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

Goes well beyond the read-only/idempotent annotations: it enumerates the payload fields an agent will get back (recids, collaborations, keywords, recordDoi, latest version, table count, hepdataUrl), states explicitly that the table values themselves are NOT returned, and discloses the 10,000-result reachability cap. These are exactly the traits an agent needs to plan a call and a follow-up.

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 purpose and the key routing hint are front-loaded, and the payload enumeration is information-dense rather than padding. The first sentence is long and clause-heavy, but every clause carries a distinct fact; only the trailing result-cap sentence is a slight add-on.

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?

The definition covers purpose, selection condition, payload contents, the explicit non-return of data values, and the hard result ceiling, with downstream routing to cern_inspire_get_paper. For a 4-parameter search tool with a full output schema, nothing an agent needs in order to call it correctly is missing.

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 baseline is 3, but the description adds meaning by naming the physics facets the query field operates over (process, observable, energy, collaboration) and by flagging that the result cap applies to the query, which reinforces the page/size limits. It does not add syntax beyond the schema's examples, so it is a modest lift above baseline.

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 verb and resource ('Find HEPData measurement records') plus the searchable dimensions (process, observable, energy, collaboration) and the data source (INSPIRE-HEP's index of HEPData submissions). This clearly distinguishes it from sibling paper-oriented tools like cern_inspire_search_literature and cern_inspire_get_paper.

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?

'when the paper is unknown' gives an explicit selection condition against the sibling tool that finds papers, and 'pass to cern_inspire_get_paper' describes the onward step. It stops short of an explicit when-not clause (e.g. 'if you already have the recid or DOI, use X instead'), so it is clear context rather than full routing.

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