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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety traits. The description adds valuable context beyond annotations: 'No API key required — open data under CC-BY 4.0' clarifies authentication and licensing, and it states what the tool returns. It does not contradict annotations and adds useful behavioral information, though pagination details are left to the schema.

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 well-structured with a clear leading purpose, illustrative examples, return information, and usage pointers. Each sentence adds value, though it is somewhat longer than necessary. It is more than a bare minimum but remains efficient and front-loaded, earning a high but not perfect score.

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 tool with 3 parameters, an output schema, and comprehensive parameter descriptions, the description covers the essential context: what it returns, how to use it in a workflow, and authentication. It lacks explicit mention of pagination behavior, but that is covered by the schema. Given the tool's simplicity and the presence of an output schema, the description is nearly complete.

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 coverage is 100%, so parameters are well-documented in the schema itself. The description adds meaning to provider_code by giving examples (WB, OECD, IMF, Eurostat) and explicitly advising to get valid codes from dbnomics.data.providers, which goes beyond the schema's description. It does not elaborate on limit/offset, but those are adequately described in the schema, so the added semantics for the key parameter justify a slightly higher than baseline score.

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 purpose: 'List all datasets published by a specific statistical agency on DBnomics.' It specifies the verb (List), resource (datasets), and scope (for a specific provider). It also distinguishes itself from siblings by indicating it returns dataset codes, names, and series counts, and points to dbnomics.data.series for browsing time series. This clearly differentiates it from related tools.

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 gives explicit usage guidance: first call dbnomics.data.providers to get valid provider codes, then use this tool to list datasets, and then use the returned dataset codes with dbnomics.data.series. It names the alternative tools and provides a clear sequence, making it obvious when to use this tool versus others.

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