list_ees_topics

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

No annotations are provided, so the description carries the full burden. It explains the output contents (topic keys, titles, descriptions, count of 23 datasets) and coverage areas, which adds useful context. However, it does not disclose whether authentication is required, if the operation is read-only, or any potential limitations (e.g., no pagination details). For a simple list tool this is adequate but not fully transparent.

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: the first front-loads the purpose, the second provides actionable guidance for using the output. Every word earns its place, with no redundancy or filler. Perfectly sized for a simple listing tool.

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 provides enough context for a tool with no parameters and no output schema: it lists what the response contains (topic keys, titles, descriptions, 23 datasets) and how to proceed next (use with discover_dataset). While it doesn't detail the exact response format, that is less critical for a simple list operation and the integration hint fills the gap.

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 tool has zero parameters, so schema coverage is trivially 100%. There is no parameter information to add, and the baseline for 0 params is 4. The description focuses on output rather than inputs, which is appropriate. No additional parameter semantics are needed.

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: 'List all available education data topics from the DfE Explore Education Statistics API.' The verb 'List' and specific resource 'education data topics' make the purpose unambiguous. It also differentiates from siblings by explicitly referencing the companion tool 'discover_dataset' and the topic key relationship.

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 explicit usage guidance by stating 'Use the topic key with discover_dataset to find the actual dataset IDs needed for querying.' This tells the agent when and how to use this tool in sequence. It does not explicitly mention when not to use it or list alternatives, but the integration advice is strong enough for a 4.

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