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

Annotations already establish read-only and idempotent behavior; the description adds concrete edge-case semantics: it is never empty on a valid need, coverage becomes 'none', and matches[] still surfaces reliable starting points. 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?

The description is compact and front-loaded, with the purpose stated before the returns and alternative. The detailed return-shape sentence is useful for anticipation but slightly redundant given an output schema exists.

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 4-parameter tool with rich annotations and an output schema, the description covers the key use case, edge behavior, and sibling routing. Nothing an agent needs to decide whether to call it 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 coverage is 100%, so the schema already explains all four parameters. The description adds a useful example of a 'need' and links reliability to the scoring concept, but it does not materially add parameter semantics beyond the schema.

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 leads with a specific verb and resource ('Map a plain-language goal ... to callable APIs, MCP servers or CLIs'), so an agent immediately knows what the tool accomplishes. It also distinguishes itself from the sibling search_registry by framing the input as a goal rather than a known keyword or slug.

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?

It states when to use the tool (plain-language goal discovery) and when not to ('For a known keyword or slug, use search_registry'). It also clarifies behavior on valid but unmatched needs, removing ambiguity about fallback use.

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