zurich_sparql

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds critical context that the endpoint is not productive and returns empty/incomplete results, which goes beyond annotations but lacks details on rate limits or authentication.

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 short and front-loaded with the warning, followed by alternatives. It is efficient, though the warning emoji and line breaks could be streamlined. Overall, it earns its place.

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 tool's simplicity (one parameter) and the presence of an output schema, the description covers the essential behavioral caveat (non-production) and usage alternatives. It is complete enough for an agent to decide not to use it.

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 description does not mention the parameter 'query' at all. However, the schema provides a detailed description for it, including an example and a tip. With schema coverage effectively providing the semantics, the description adds no extra value, so baseline 3 is appropriate.

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 that the tool is for SPARQL queries but is not productive, which distinguishes its purpose. The title from annotations ('SPARQL-Abfrage (Linked Data)') reinforces this, and the warning is explicit.

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 explicitly advises against using this tool due to non-production status and recommends specific alternatives (zurich_search_datasets, zurich_datastore_query, zurich_datastore_sql), providing clear when-to-use and when-not-to-use guidance.

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