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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, which covers the safety profile. The description adds context by stating that it returns matching entries with a specified set of fields, which is useful for understanding the output. However, it does not disclose additional behavioral traits such as matching behavior (case sensitivity, partial match) or rate limits. Given the annotations, the description adds moderate value, so a 3 is appropriate.

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 exactly three sentences, each earning its place: the first states the function, the second lists the return fields, the third gives the use case. It is front-loaded with the verb and resource, and contains no filler or repetition. It effectively communicates the essential information in an efficient, structured manner.

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 description covers the source (CERN Open Data glossary), input (term, limit), output (matching entries with listed fields), and use case (explaining HEP concepts). It is adequate for a simple lookup with two parameters and read-only annotations. It does not mention error behavior or partial-match edge cases, but given the output schema exists and the annotations, the description is nearly complete. A 4 is fair, slightly below a 5 because of the absence of any caveat about ambiguous searches.

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 both `term` and `limit` are already documented in the input schema. The description lists example terms like 'quark', 'hadron', and 'CMSSW', which aids an agent in formulating queries, but these are illustrative rather than adding semantic meaning that the schema lacks. Baseline 3 applies because the schema carries the parameter semantics.

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 ('Look up') and a precise resource ('particle physics and high-energy physics (HEP) terms from the CERN Open Data glossary'). It also enumerates the return fields (term, aliases, definition, category, related terms, links), making the tool's function unmistakable. The context for being used alongside other CERN tools is clear: it is for glossary terminology, not dataset browsing or record search.

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 explicitly states the tool is 'Ideal for AI agents that need to explain or understand HEP concepts', giving a clear usage context. It does not explicitly mention when not to use it or name alternative tools, but the specificity of the glossary function inherently differentiates it from sibling tools, and the guidance is sufficient for an agent to decide.

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