search_education_statistics

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

No annotations exist, so the description bears the full burden. It clearly states that the tool returns publication IDs only, not the actual data, and directs users to other tools for data access. This sets expectations about the tool's output and its role in the pipeline. However, it does not discuss result limits, pagination, or search behavior details, which would further enhance transparency.

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 two sentences long. The first sentence states the purpose and gives examples; the second sentence explains the return value and the subsequent steps. Every word earns its place—no fluff, no repetition. It is front-loaded and easily scannable.

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?

For a simple search tool with one parameter, the description delivers complete context. It doesn't just say 'search for publications'; it explains what the results are (IDs), how they integrate into the broader tool ecosystem, and hints at the types of topics covered. No output schema exists, but the description clarifies what the return value will be used for, which is adequate for the tool's simplicity.

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 single parameter 'topic' is fully described in the input schema with examples. The description repeats these examples but does not add new meaning beyond what the schema provides. The schema coverage is 100%, so the baseline of 3 is appropriate; the description adds marginal value by reinforcing the types of topics, but it doesn't clarify syntax or search nuances.

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 uses a specific verb ('Search') and identifies the exact resource ('DfE Explore Education Statistics catalogue'). It also lists example topics, making the scope clear. By contrasting with 'get_publication_datasets', 'get_dataset_metadata', and 'query_dataset', it effectively distinguishes this search tool from its siblings.

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 a clear workflow: search for topics, get publication IDs, then use those IDs with 'get_publication_datasets' and 'get_dataset_metadata/query_dataset'. This implies this tool is the entry point for data discovery. It does not explicitly mention when to avoid using this tool or list alternative search tools, but the workflow guidance is strong enough.

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