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Send feedback

send_feedback

Send the user's feedback to the Pressure Atlas team: a feature request, a complaint, a bug, praise, or anything else. The team reads it to decide what to build next. Offer it when the user wants something the tools do not do, reaches a limit, is confused or frustrated, or says what they like or dislike, at most once per conversation unless the user brings it up. Ask the user first and send what they said; never send feedback they did not ask for or agree to. Does not count against the daily quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesfeature_request (something they want), complaint, bug (something wrong), praise, or other.
aboutNoOptional: the tool, dataset, or topic it concerns, e.g. "get_recent_pressure".
messageYesThe user's feedback in their own words (3-2000 characters). Include what they were trying to do.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYesduplicate: the same words were received within a day; nothing new was stored.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations mark this as a non-read-only, non-idempotent write, and the description adds genuinely new behavior: the message is delivered to a human team, it requires explicit user consent, and it does not consume the daily quota. The rate-limit and consent semantics go well beyond what the structured fields convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the action and resource, then moves through routing triggers, frequency limit, and consent requirement in three dense sentences with no filler. Every clause carries a distinct operational instruction.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return values need no explanation, and the description covers the remaining gaps an agent would face: consent requirement, frequency limit, quota exemption, and delivery destination. Nothing needed to invoke this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so kind, about, and message are already fully documented including the enum values and length bounds. The description reinforces that the message should be the user's own words, but adds no format or syntax detail beyond the schema; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (send) and resource (the user's feedback to the Pressure Atlas team), then enumerates the accepted kinds. This clearly demarcates it from all sibling tools, which are read-only data retrieval operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly lists trigger conditions (user wants something the tools don't do, hits a limit, is confused or frustrated, expresses like/dislike), states the frequency ceiling (at most once per conversation unless the user raises it), and mandates asking the user first. Both when-to-use and when-not-to-use are covered.

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

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