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by com.buckybuild
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Submit feedback to Bucky

submitBuckyFeedback
Idempotent

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

TableJSON 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."
llm_modelYesThe 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.
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.
conversation_idNoEcho 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.
affectedToolNameNoTool the user is commenting on, when already known

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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"
+]
  2. 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"
+]
  3. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses that the tool opens an interactive form requiring human acceptance, and that nothing is saved unless the human accepts. This goes beyond the annotations (idempotentHint, readOnlyHint, destructiveHint) by explaining the human-in-the-loop nature, which is critical for the agent to understand the tool's behavior.

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?

The description is concise and front-loaded: it states the core purpose first, then usage conditions, then the interaction model. Every sentence adds value, with no redundant or generic phrasing. It is appropriately sized for the tool's complexity.

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 tool with no output schema, the description covers the essential interaction (form display, human acceptance) and usage boundaries. Combined with the detailed parameter schema, an agent has everything needed to decide when to call it and what to expect. No critical missing context.

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?

The input schema already provides 100% description coverage for all 5 parameters, including detailed guidance on the required 'context' and 'llm_model' fields. The tool description adds no extra parameter-level meaning, so a baseline score of 3 is appropriate per the rubric.

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 description clearly states a specific verb ('Open a human-reviewed feedback form and submit it') and resource ('Bucky product team'), and distinguishes it from silent self-reporting. It makes the tool's purpose unmistakable even without the title.

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?

The description explicitly lists the exact user intents that warrant calling this tool ('send feedback, report a problem, suggest an improvement, or praise something') and gives a hard exclusion ('Do not use for silent agent self-reporting'). This leaves no ambiguity about when to use it.

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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