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send_feedback

Destructive

Send the user's feedback (a bug, an idea, a question or a general comment) straight to the Vendooly team — they read every one. Use it whenever the user reports a problem, is frustrated, hits an error, or suggests something. ALSO proactively OFFER to send it when something clearly is not working ("vuoi che lo segnali al team Vendooly?") and call it once they agree. Put the user's words in message; use ai_context to summarize what they were trying to do, which account/tool, and any error you saw. Confirm to the user once it is sent.

Input Schema

NameRequiredDescriptionDefault
messageYesThe feedback in the user's own words (what happened / what they want).
categoryNoType of feedback (default other).
ai_contextNoShort note: what the user was doing, which account/tool, any error message you observed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, openWorldHint=true, and destructiveHint=true. The description adds useful behavioral context: feedback goes externally to the Vendooly team, the team reads every one, and the agent should offer before sending and confirm after. It does not address data handling or rate limits, so it is strong but not exhaustive.

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?

Purpose is front-loaded in the first sentence, and each following sentence delivers distinct guidance: usage triggers, proactive offering, parameter filling, and confirmation. There is little redundancy and no filler.

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?

For a simple three-parameter write tool with no output schema, the description covers purpose, when to use it, how to fill key parameters, and what to tell the user after sending. An agent has enough information to invoke it correctly.

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%, so the baseline is 3. The description goes beyond the schema by specifying what to put in `message` (the user's own words) and `ai_context` (what they were doing, account/tool, errors), improving parameter usage. It adds little for `category` beyond the enum already in the schema.

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?

The first sentence names a specific verb ('Send') and resource ('the user's feedback') and lists the content types. It also identifies the recipient ('Vendooly team'), so the agent can select it unambiguously among the many ads/listing siblings.

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?

Explicit when-to-use triggers are given: user reports a problem, is frustrated, hits an error, or suggests something. It also instructs proactive offering with a sample phrase and says to call the tool only after the user agrees, which is unusually clear routing guidance.

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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