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send_feedback

Read-onlyIdempotent

Send feedback about this server to its developers. Most important: report a missing capability whenever no available tool fits your task, even if you can work around it (feedback_type "missing_capability"). Also welcome: a tool that failed or confused you, an unhelpful error, or something that worked well. This records the feedback; it does not add or change tools. Do not include user PII or sensitive content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailsNoOptional longer context: what you tried, exact parameter values, the error text you saw, and any workaround you used. Omit when the summary says it all.
summaryYesOne self-contained sentence. For 'missing_capability': the capability you needed, e.g. 'No tool to delete multiple cohorts in one call.' For 'issue': the tool and the problem, e.g. 'query-trends rejects relative date ranges with an unclear error.'
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.
sentimentNoOptional: how the experience felt overall.
tool_nameNoOptional: the existing tool this feedback is about (for 'issue' or 'praise'). Leave empty for 'missing_capability' - the point is that no tool fits.
feedback_typeYesWhat kind of feedback this is. Use 'missing_capability' when the tool you needed does not exist in the tool list - nothing failed, the capability is absent (this is the most valuable report; send it even if you found a workaround). Use 'issue' when an existing tool behaved badly: it failed, returned a confusing error, or its description or schema misled you. Use 'praise' when something worked notably well. Use 'other' for anything else.
task_completedNoOptional: whether you still completed the user's task despite the problem.
conversation_idNoPass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.
friction_pointsNoOptional: the specific moments that slowed you down, as short bullet-like sentences, quoting exact tool names, parameters, or error text.
suggested_improvementNoOptional: the concrete change that would have helped, e.g. the tool to add, the description to reword, or the error message to improve.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations cover readOnly/idempotent/non-destructive, so the safety profile is handled, and the description adds the crucial behavioral fact that this records feedback without adding or changing tools, plus a PII/sensitive-content prohibition. It does not mention whether feedback is reviewed, rate limits, or what happens after submission, but for a fire-and-forget reporting tool that is a minor gap.

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 tight sentences that front-load the primary instruction (report missing capabilities whenever no tool fits) before the secondary cases and the important constraints. Every sentence carries non-redundant information, including the PII warning placed last as a guardrail.

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 feedback-reporting tool with no output schema and full schema description coverage, the description supplies exactly the missing pieces: the priority ordering of feedback types, the fact that nothing is added or changed, and the content restriction. Nothing an agent needs in order to call it correctly is absent.

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 coverage is 100%, so the schema already documents all ten parameters thoroughly, including enum values, examples, and edge-case instructions (conversation_id must be passed unchanged and never invented, llm_model must be 'unknown' rather than guessed). The description adds no parameter meaning beyond what is in the schema, so the baseline 3 is correct and appropriate.

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+resource (send feedback to developers) with an unusually clear statement of primary purpose, weights the missing_capability case explicitly, and distinguishes itself from every sibling (all image/video tools) by being the only meta/reporting tool.

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 names when to use it (report missing capability whenever no tool fits, even with a workaround) and enumerates the other cases (failed tool, confusing error, praise, other) with feedback_type values attached. Conditions are attached to categories, so the agent knows which type to pick.

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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