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Submit feedback to Bucky

submitBuckyFeedback

Open a human-reviewed feedback form and submit it to the Bucky product team. Call only when the user explicitly asks to send feedback, report a problem, suggest an improvement, or praise something. The server asks the MCP client to show the form; nothing is saved unless the human accepts it. Do not use for silent agent self-reporting.

Input Schema

NameRequiredDescriptionDefault
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
submissionIdNoOptional. A UUID for this submission; generated server-side when omitted. Pass the same UUID when retrying this exact feedback so it is not recorded twice.
affectedToolNameNoTool the user is commenting on, when already known

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / properties / llm_model
      Removed value: -{
      -  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
      -  "type": "string"
      -}
    • changedInput schema / required
      Previous value: -[
      -  "context",
      -  "llm_model"
      -]New value: +[
      +  "context"
      +]
  2. Changed1 schema field changed
    • removedInput schema / properties / conversation_id
      Removed value: -{
      -  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
      -  "type": "string"
      -}
  3. Changed4 schema fields changed
    • changedInput schema / properties / context / description
      Previous value: -"Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\""New value: +"Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    • addedInput schema / properties / conversation_id
      Added value: +{
      +  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
      +  "type": "string"
      +}
    • addedInput schema / properties / llm_model
      Added value: +{
      +  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
      +  "type": "string"
      +}
    • changedInput schema / required
      Previous value: -[
      -  "context"
      -]New value: +[
      +  "context",
      +  "llm_model"
      +]
  4. Changed2 schema fields changed
    • changedInput schema / properties / submissionId / description
      Previous value: -"A new UUID generated for this submission. Reuse the same UUID when retrying this exact feedback."New value: +"Optional. A UUID for this submission; generated server-side when omitted. Pass the same UUID when retrying this exact feedback so it is not recorded twice."
    • changedInput schema / required
      Previous value: -[
      -  "submissionId",
      -  "context"
      -]New value: +[
      +  "context"
      +]
  5. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false, destructiveHint=false, openWorldHint=false and idempotentHint=false, but the description adds genuinely non-obvious behavior: the server asks the MCP client to render the form, and nothing persists unless the human accepts it. That is the key safety fact an agent needs. It stops short of covering retry/deduplication behavior, which is stranded in the submissionId parameter instead.

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?

Four short sentences: what it does, when to call it, what happens on the server, and the exclusion. The exclusion is placed last as a guard rather than buried mid-paragraph, and nothing is repeated.

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 three-parameter tool with complete schema coverage and no output schema, the description covers purpose, trigger conditions, the human-in-the-loop flow, and the no-persistence guarantee. An agent has everything needed to decide to call it and what to expect afterward.

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 all three parameters are already documented in structured form, and the description adds nothing about them. Baseline 3 applies since the schema carries the full burden, including the idempotency semantics for submissionId.

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: 'Open a human-reviewed feedback form and submit it to the Bucky product team.' No sibling tool does anything similar, and the human-reviewed framing distinguishes it from any programmatic reporting path.

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 activation conditions ('only when the user explicitly asks to send feedback, report a problem, suggest an improvement, or praise something') and an explicit exclusion ('Do not use for silent agent self-reporting'). Both the when and the when-not are stated, not inferred.

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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