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

Annotations provide no behavioral hints (all false), so the description carries the burden. It transparently explains the token flow ('Filing without an account returns a claim_token; pass it back later...to read whether it was fixed'), rate limiting ('Rate-limited to 5 per identifier per day'), and cost ('Free; doesn't count against your tool-call quota'). It also indicates the impact ('The team reads digests daily and signal directly affects roadmap').

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 dense but every sentence serves a purpose. It front-loads the purpose, then flows into when-to-use, scope boundaries, message guidelines, token mechanics, and operational details—all in a logical sequence. Nothing is redundant or extraneous given the tool's complexity.

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?

The tool has 4 parameters, a nested object, and no output schema, but the description covers all behavioral and invocation nuances. It explains the return value implicitly (claim_token), rate limits, cost, and the intended audience. This is a fully specified description for an agent to correctly select and call the tool.

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 baseline is 3. The description adds meaningful value by showing how the claim_token is used in practice: 'pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})'. It also provides content guidance for the message and context fields: 'Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt.' This goes beyond the schema's per-field descriptions.

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 specific action: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clarifies the resource (Pipeworx team) and enumerates the exact feedback categories. It also sets boundaries by distinguishing from sibling tools that ask questions or discover tools, making it uniquely positioned as the feedback channel.

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?

Explicit guidance is given: '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 an exclusion—tools from other MCP servers should be reported elsewhere—and suggests an alternative action: 'file it with that server instead.' This fully covers when and when not to use.

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