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

The description reveals behavioral traits far beyond the annotations: rate-limiting ('Rate-limited to 5 per identifier per day'), the return of a claim_token when filing without an account, and how to use that token later to read resolution status. It also mentions that the team reads digests daily and signals affect the roadmap, and that the tool is free. Annotations are all false but uninformative; the description carries the full burden and excels.

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 every sentence is purposeful: scope, use cases, exclusions, claim_token mechanics, rate limits, and cost. It is front-loaded with the core purpose and then provides operational details. While not as minimal as a two-sentence example, the density is justified by the tool's nuanced behavior.

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?

Despite no output schema, the description explains what the tool returns (a claim_token) and how to use it later. It covers the full feedback lifecycle: reporting, following up, and resolution. This is a complete picture for an agent to invoke the tool effectively in various scenarios.

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% (all four parameters have descriptions), so a baseline of 3 applies. The description adds value beyond the schema: it clarifies the claim_token usage pattern ('pass it back later... to read whether it was fixed'), emphasizes describing issues in terms of Pipeworx tools/packs rather than pasting user prompts, and relates the 'type' values to concrete scenarios. This pushes it to a 4.

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 opens with a clear directive: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' This specifies the action (tell), the resource (Pipeworx team), and the scope of feedback (broken, missing, or needed). It also distinguishes this tool from sibling tools by stating it is ONLY for tools served by this Pipeworx connection, not for tools from other MCP servers.

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 defines when to use the tool: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also provides a clear exclusion: 'if the tool came from a different MCP server... file it with that server instead.' This is a textbook example of 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.