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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: it specifies the dataset type restriction, pagination support, dataset counts per experiment, and run-period energy details. These are useful behavioral traits not covered by annotations. No contradictions found.

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 compact and well-structured, starting with the core action and then layering relevant specifics. It packs useful details (dataset counts, run energies, experiment names) without redundancy or fluff. Every sentence contributes value, and the length is appropriate for the tool's complexity.

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 that an output schema exists and annotations cover safety, the description sufficiently covers filtering options, dataset scope, pagination, and run-period context. It does not explicitly mention default behavior when no parameters are provided or error handling, but these are minor gaps. Overall, it is complete for an agent to call the tool 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?

The input schema has 100% coverage with descriptions for all parameters, so the baseline is 3. The description adds meaningful examples and context: it maps run periods to energies, provides experiment-specific dataset counts, and gives sample keyword values (NanoAOD, MiniAOD). This enriches understanding beyond the schema, earning a 4.

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's function: browsing and filtering CERN Open Data datasets by multiple dimensions. It specifies the resource (collision and simulated datasets) and differentiates from sibling tools by noting it returns only records of type Dataset, with specific examples like NanoAOD and MiniAOD. This is precise and distinguishable.

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 clear use cases, such as finding simulation samples or data for a given LHC run period, and implicitly distinguishes from broader record search by restricting to datasets. However, it does not explicitly name alternative tools or state when NOT to use it, leaving some room for inference. Still, the context is helpful.

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