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Send feedback to Unl

submit_feedback

Send the human's feedback to the Unlimitless TEAM — the one OUTBOUND-to-vendor verb (the other six act on the human's own substrate; this is cross-workspace). Sends only on the human's explicit gesture: a FEATURE-SHAPED WANT → type 'suggestion'; a FRUSTRATION ABOUT Unl itself → type 'feedback'; a GRIEVANCE THE PERSON WANTS ANSWERED → type 'complaint' (acknowledged with a 48h response commitment and tracked). You supply ONLY {type, body} — the user's email + identity are taken from the authenticated session, never from you (no spoofing). One submission = one team-side record + a Slack ping + an ack emailed to the human. Never call it silently or to write anything to the human's own substrate: it sends their words to Unlimitless rather than into their own record, so they should know it is going.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYesThe feedback in the human's terms — what they want or what's frustrating. Required.
typeYes'suggestion' = a feature-shaped want; 'feedback' = a frustration about unl itself; 'complaint' = a grievance the person wants ANSWERED — it is acknowledged with a 48h response commitment and tracked, so use it when someone is not merely noting a problem but expects a reply.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / type / description
      Previous value: -"'suggestion' = a feature-shaped want; 'feedback' = a frustration about unl itself; 'complaint' = a grievance the person wants ANSWERED (784c7f44) — it is acknowledged with a 48h response commitment and tracked, so use it when someone is not merely noting a problem but expects a reply."New value: +"'suggestion' = a feature-shaped want; 'feedback' = a frustration about unl itself; 'complaint' = a grievance the person wants ANSWERED — it is acknowledged with a 48h response commitment and tracked, so use it when someone is not merely noting a problem but expects a reply."
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Despite annotations covering the safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=false), the description adds substantial context: it fires only on an explicit gesture, sends the human's words externally, takes email/identity from the authenticated session (no spoofing), produces a team-side record + Slack ping + ack email, and carries a 48h response commitment for complaints. This is rich behavior beyond the structured fields.

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

Conciseness4/5

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

Front-loads purpose, then routing, then behavioral consequences; every clause is relevant. It is on the heavy side with all-caps emphasis and some overlap with the enum descriptions in the schema, but not wasteful.

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 no output schema, the description carries the return burden and does so: one team-side record, a Slack ping, and an emailed acknowledgement. For a 2-parameter, non-destructive submission tool, an agent has everything needed to call it correctly and safely.

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

Parameters4/5

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

Schema coverage is 100% and the enum values are already documented in the schema, but the description reframes the enum in decision terms ('feature-shaped want', 'frustration about Unl', 'grievance the person wants answered') and emphasizes body is in the human's own terms. This adds interpretive value over the schema, though it largely mirrors the enum definitions.

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 and resource (send the human's feedback to the Unlimitless TEAM) and explicitly positions it against its siblings as 'the one OUTBOUND-to-vendor verb (the other six act on the human's own substrate; this is cross-workspace).' An agent can distinguish it from log_to_unl/save_to_unl/write_to_tool without opening any schema.

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?

Gives explicit when-to-use routing (feature-shaped want → suggestion; frustration about Unl → feedback; grievance the person wants answered → complaint) and an explicit when-not (never call it silently, never to write to the human's own substrate). Nothing about selection or prerequisites is left to inference.

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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