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AI Design Blueprint

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signals.feedback

Public: records explicit free-text user feedback about the Blueprint, this tool surface, or a specific principle/example. Captures category (bug, doctrine_critique, missing_example, ergonomics, other), free-text body, and optional contact_email when permission_to_follow_up is true. WHEN TO CALL: ONLY when the user explicitly says they want to give feedback (e.g. 'can you log this as feedback', 'file this critique', 'send a bug report'). Use signals.report instead for value-moment metrics (rating validate's output 1-5). WHEN NOT TO CALL: proactively, silently, or to substitute for signals.report. Never harvest contact info without explicit permission_to_follow_up=true. BEHAVIOR: write-only, no auth required (open to all callers). Stores one feedback record, at rest in the UK. contact_email is stored ONLY when permission_to_follow_up=true, and that fact is confirmed back in the response so the user can see the privacy boundary. When permission_to_follow_up=true AND a valid contact_email is given, and only then, it also opens a support inbox item so a human can reply, and the free-text fields are sent to OpenAI for triage (severity and a short summary). The contact_email field is never part of that request, so keep personal data out of the free text. Separately, feedback linked to an account may appear, pseudonymised and shortened, in our weekly lead-intelligence extract to OpenAI (US). private_session does not apply to this tool: it runs only when the user chooses to give feedback. REJECTION CODES (invalid_request): a call with no rating, no what_helped or what_missing comment and no would_use_again answer (task_type, surface and contact_email alone are not feedback); a rating outside 1-5; a contact_email that is not one plain address.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
surfaceNoWhich Blueprint surface the feedback is about. Use 'mcp' if the session was via Claude Code or another MCP client. Use 'principles', 'examples', 'guides', 'coaching', or 'validation' based on what the user interacted with.
task_typeNoWhat the user was doing when they decided to give feedback. Use plain English: e.g. 'code-review', 'architecture-design', 'agent-setup', 'onboarding', 'validation'. Infer from context.
what_helpedNoAsk the user: 'What was most helpful?' Record their answer verbatim or paraphrased in plain English. Max 1000 chars. No code snippets, no proprietary content.
what_missingNoAsk the user: 'What was missing or could be improved?' Record their answer verbatim or paraphrased. Max 1000 chars.
contact_emailNoOnly ask for this if the user explicitly says they want a follow-up response. Never prompt for email unprompted. Only stored when permission_to_follow_up=true.
rating_clarityNoAsk the user: 'How clear was the Blueprint guidance? Rate 1–5.' 1 = very unclear, 5 = very clear. Only set if the user gives an explicit number.
would_use_againNoAsk the user: 'Would you use the Blueprint again for a similar task?' Set true/false based on their answer. Only set if they answer explicitly.
rating_usefulnessNoAsk the user: 'How useful was the Blueprint for this task? Rate 1–5.' 1 = not useful, 5 = very useful. Only set if the user gives an explicit number.
permission_to_follow_upNoSet to true only if the user explicitly said they want a follow-up. Must be confirmed before storing contact_email.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / task_type / description
      Previous value: -"What the user was doing when they decided to give feedback. Use plain English — e.g. 'code-review', 'architecture-design', 'agent-setup', 'onboarding', 'validation'. Infer from context."New value: +"What the user was doing when they decided to give feedback. Use plain English: e.g. 'code-review', 'architecture-design', 'agent-setup', 'onboarding', 'validation'. Infer from context."
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "submit_feedbackDictOutput",
+  "type": "object"
+}
  3. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "$defs": {
-    "ErrorBody": {
-      "description": "The body of `error_payload(code, message, details)` returns.",
-      "properties": {
-        "code": {
-          "title": "Code",
-          "type": "string"
-        },
-        "details": {
-          "additionalProperties": true,
-          "title": "Details",
-          "type": "object"
-        },
-        "message": {
-          "title": "Message",
-          "type": "string"
-        }
-      },
-      "title": "ErrorBody",
-      "type": "object"
-    }
-  },
-  "properties": {
-    "deduplicated": {
-      "default": null,
-      "title": "Deduplicated",
-      "type": "boolean"
-    },
-    "error": {
-      "$ref": "#/$defs/ErrorBody",
-      "default": null
-    },
-    "event_id": {
-      "default": null,
-      "title": "Event Id",
-      "type": "string"
-    },
-    "event_type": {
-      "default": null,
-      "title": "Event Type",
-      "type": "string"
-    },
-    "guidance": {
-      "default": null,
-      "title": "Guidance",
-      "type": "string"
-    },
-    "status": {
-      "default": null,
-      "title": "Status",
-      "type": "string"
-    },
-    "surface_used": {
-      "default": null,
-      "title": "Surface Used",
-      "type": "string"
-    }
-  },
-  "title": "SignalsResponse",
-  "type": "object"
-}New value: +null
  4. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$defs": {
+    "ErrorBody": {
+      "description": "The body of `error_payload(code, message, details)` returns.",
+      "properties": {
+        "code": {
+          "title": "Code",
+          "type": "string"
+        },
+        "details": {
+          "additionalProperties": true,
+          "title": "Details",
+          "type": "object"
+        },
+        "message": {
+          "title": "Message",
+          "type": "string"
+        }
+      },
+      "title": "ErrorBody",
+      "type": "object"
+    }
+  },
+  "properties": {
+    "deduplicated": {
+      "default": null,
+      "title": "Deduplicated",
+      "type": "boolean"
+    },
+    "error": {
+      "$ref": "#/$defs/ErrorBody",
+      "default": null
+    },
+    "event_id": {
+      "default": null,
+      "title": "Event Id",
+      "type": "string"
+    },
+    "event_type": {
+      "default": null,
+      "title": "Event Type",
+      "type": "string"
+    },
+    "guidance": {
+      "default": null,
+      "title": "Guidance",
+      "type": "string"
+    },
+    "status": {
+      "default": null,
+      "title": "Status",
+      "type": "string"
+    },
+    "surface_used": {
+      "default": null,
+      "title": "Surface Used",
+      "type": "string"
+    }
+  },
+  "title": "SignalsResponse",
+  "type": "object"
+}
  5. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Adds substantial context beyond the annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false): write-only/no-auth, UK data residency, contact_email stored only when permission_to_follow_up=true, opening of a support inbox item, free text sent to OpenAI for triage, weekly pseudonymised lead-intelligence extract to OpenAI (US), and explicit rejection codes. This is far beyond what structured fields convey.

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-loaded with the purpose, then structured under WHEN TO CALL / WHEN NOT TO CALL / BEHAVIOR / REJECTION CODES headers. Dense but mostly earns its length given the privacy and data-flow complexity; a few clauses could be tightened.

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 9-parameter, privacy-sensitive, all-optional mutation tool with an output schema, this is comprehensive: it covers storage, follow-up permission, downstream data flows, private_session applicability, and validation rejection criteria. Because an output schema exists, return-value detail is appropriately not required.

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 baseline is 3. The description does add some meaning (the contact_email/permission_to_follow_up gating and the validity rule that a rating, what_helped/what_missing, or would_use_again answer is required). However, it also references non-existent 'category' and 'body' fields, so it does not clearly rise above the schema-documented baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('records explicit free-text user feedback about the Blueprint, this tool surface, or a specific principle/example') and explicitly distinguishes itself from signals.report. Minor deduction: it claims to capture 'category (bug, doctrine_critique, ...)' and a 'free-text body', neither of which exists in the schema, which slightly muddies what the tool actually records.

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 CALL with trigger examples ('can you log this as feedback'), an explicit WHEN NOT TO CALL ('proactively, silently, or to substitute for signals.report'), and names the sibling alternative with the condition that selects it. Nothing 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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