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

Annotations already declare readOnly, openWorld, and idempotent behavior, so a lower burden remains. The description adds valuable behavior context beyond those annotations: 'No API key required — open data under CC-BY 4.0,' which informs authentication and licensing expectations. It also conveys the scope of the response with examples of included agencies.

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 front-loaded with the core action, then provides useful context: included agencies, return fields, downstream usage, and license. The statistics about 46,000 datasets and 1.7 billion series are slightly promotional and not strictly necessary, but the overall length is still reasonable and focused.

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 the simple 2-parameter schema, the presence of an output schema, and robust annotations, the description covers everything an agent needs: what is returned, how to proceed to datasets, and auth/licensing. No meaningful operational detail such as rate limits or required fields 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 description coverage is 100%, and both limit and offset already carry clear descriptions including ranges and defaults. The tool description does not add parameter-level meaning, so the baseline score of 3 is appropriate; the schema does the work.

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 and resource: 'List all statistical data providers available on DBnomics.' It clearly states what the tool returns ('provider codes, full names, and regions') and distinguishes itself from the related dbnomics.data.datasets tool by positioning provider codes as an input for dataset exploration. This is unambiguous and differentiated.

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 practical context: it is the entry point for discovering available providers, and it explicitly tells the agent to use the returned provider codes with dbnomics.data.datasets. It does not explicitly name exclusions or when-not-to-use alternatives, but the workflow guidance is concrete enough for correct selection.

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