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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value beyond these: it discloses the return content ('table title, dimension codes, and all available value codes with German labels') and the data licensing/authorization posture ('Swiss OGD, no auth, commercial use permitted'). No contradiction with annotations.

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?

Four sentences, each earning its place: purpose, workflow, example, licensing. No filler or repetition of schema content. The most decision-relevant information (what it returns and where the input comes from) is front-loaded.

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?

Complete for a single-parameter, read-only metadata lookup tool with an output schema present. The description covers purpose, input provenance, output shape, downstream use, a worked example, and licensing. Rate limits and pagination are not material for a dimension-metadata endpoint, so nothing an agent needs to invoke it correctly 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%, so the schema fully documents database_id with two concrete examples and the instruction to obtain it from catalog.list. The description reinforces this by saying 'Provide a database_id from catalog.list', which is useful but redundant. Baseline 3 is appropriate since the schema carries the semantic load.

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?

States a specific verb and resource: 'Get the variable dimensions and available filter values for any Swiss Federal Statistical Office (FSO/BFS) STAT-TAB dataset.' Distinguishes itself from siblings by describing its role as the dimension/filter explorer, distinct from catalog.list (dataset discovery), table.query (data retrieval), and wages.monthly (specific dataset). The concrete example with 'px-x-0304010000_201' and its returned dimensions makes the purpose unmistakable.

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?

Explicitly frames the tool's position in the workflow: 'Provide a database_id from catalog.list' establishes the upstream dependency, and 'needed to construct filters for table.query' names the downstream consumer. An agent knows exactly when to call this tool — after catalog.list and before table.query. No alternative routing is left to inference.

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