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 and mutability. The description adds critical behavioral context beyond annotations: it explains the data source (published evidence context), the meaning of absent fields, that it is not a freshness guarantee, that DataPulse requires no API key, and the rate limit (one request per second with small burst). This is strong but not perfect—it could clarify the response shape or error handling, but it goes far beyond what annotations provide.

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 long but front-loaded with the primary use case and return-list summary, then provides usage routing, behavioral caveats, and rate limits. Every sentence serves a purpose, but the density is high and some details (e.g., 'compact published evidence context') are repeated. It is structured logically and not redundant, so it earns a 4.

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?

This is a complex tool with a rich output schema, but the description covers all essential context: when to use, what it returns, what it does not do, the data source semantics, rate limits, and how it fits with sibling tools. Given the existence of an output schema, the description does not need to detail return values, and it is complete enough for an agent to call it correctly. A 5 is justified.

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 already documents the parameter well, including examples and the note about preserving order. The description adds value by explaining the expected format (dataset slugs from search_datasets) and the relationship to get_provenance, which is useful for correct invocation. It slightly over-extends by mentioning get_dataset for display-name resolution, but that is helpful context. Baseline 3 is exceeded because of the added guidance, but not a 5 since the description doesn't add much beyond what the schema has.

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 ('get') and resource (provenance) and clearly enumerates what the tool returns (source, steward, licence/attribution, canonical URL, evidence context, etc.). It is distinct from siblings like get_evidence and verify_evidence, and the description explicitly contrasts it with those tools, so an agent can tell them apart.

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 states when to use the tool ('Use when asked can I cite this source?', for citation-ready provenance) and when not to use it ('do not use it for full evidence or a live comparison—use get_evidence or verify_evidence instead'). Provides a recommended pipeline ('search_datasets → verify_dataset → get_provenance') and notes rate limits and retry behavior. This is exemplary guidance.

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