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

Annotations provide no safety signals (all false), so the description carries the burden. It adds useful behavioral context: 'Rate-limited to 5 per identifier per day' and 'Filing without an account returns a claim_token; pass it back later.' It also mentions 'Free; doesn't count against your tool-call quota,' which is beyond structured data. 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 a single dense paragraph but every sentence adds value: purpose, use cases, exclusions, message guidelines, token mechanics, daily digest, rate limit, and quota note. It is somewhat long but not wasteful; a structured list could improve scannability.

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 feedback tool with no output schema, the description covers essential operational aspects: what to report, what not to report, how to report, token-based follow-up, rate limiting, and impact. It also clarifies that this tool is free and doesn't consume quota. No significant gaps for an AI agent to invoke it correctly.

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 baseline is 3. The description goes further by explaining the claim_token usage pattern ('pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed') and constraining message content: 'Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt.' This adds value beyond the schema's parameter 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 clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It uses a specific verb ('tell') and resource ('Pipeworx team'), and distinguishes from siblings by focusing on feedback submission rather than research/query tools like ask_pipeworx or discover_tools.

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 when-to-use guidance is provided: '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 gives exclusions: 'ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server ... file it with that server instead.' This clearly directs when to use this tool versus alternatives.

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