AI Design Blueprint Doctrine

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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), single insert into UserFeedback. UK/EU residency. 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.

Input Schema

TableJSON Schema

Name	Required	Description
`surface`	No	Which 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_type`	No	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.
`what_helped`	No	Ask 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_missing`	No	Ask the user: 'What was missing or could be improved?' Record their answer verbatim or paraphrased. Max 1000 chars.
`contact_email`	No	Only 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_clarity`	No	Ask 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_again`	No	Ask 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_usefulness`	No	Ask 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_up`	No	Set to true only if the user explicitly said they want a follow-up. Must be confirmed before storing contact_email.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.8/5.0

Behavior5/5

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

Despite annotations being minimal (readOnlyHint, destructiveHint false), the description thoroughly discloses behavior: write-only, no auth required, single insert, UK/EU residency, and privacy handling of contact_email confirmed in response. No annotation contradiction.

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?

Description is detailed but well-structured with clear sections (WHEN TO CALL, WHEN NOT TO CALL, BEHAVIOR). Front-loaded with purpose. A minor redundancy in listing parameters already in schema, but overall efficient given 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?

Fully covers usage context, parameter handling, privacy, alternative tools, and behavioral traits. With no required parameters and an output schema, the description leaves no gaps for an AI agent to infer incorrectly.

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%, baseline is 3. The description adds significant value by explaining when to ask for each parameter (e.g., permission_to_follow_up), how to prompt the user, and constraints like maxLength. Slightly above baseline.

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 the tool records explicit user feedback, lists the captured components (category, body, contact_email), and distinguishes from signals.report. Verb+resource is specific and sibling differentiation is explicit.

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?

Provides explicit WHEN TO CALL (only when user says they want to give feedback), WHEN NOT TO CALL (proactively, silently, or substitute for signals.report), and an alternative tool (signals.report) with clear use case distinction.

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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TDQS

A4.6/5.0

Disambiguation5/5

Each tool targets a clearly distinct purpose. For example, architect.validate vs architect.validate_consensus differ in single-shot vs consensus; handoffs.agency, handoffs.operator, and handoffs.partnership are separated by engagement type. No significant overlap.

Naming Consistency4/5

Tools follow a consistent dot-notation grouping (architect.*, clusters.*, examples.*, guides.*, handoffs.*, me.*, principles.*, signals.*, team.*) with predictable verbs (validate, list, get, search, add, etc.). Minor deviation: some underscore within names (e.g., me.add_evidence) but overall pattern holds.

Tool Count4/5

24 tools is on the higher side but justified given the broad domain spanning validation, certification, learning, handoffs, and feedback. Each tool has a clear role, and the count reflects the platform's comprehensive scope without feeling bloated.

Completeness5/5

The tool surface covers the full workflow (validate → consensus → certify), discovery (principles, clusters, examples, guides), personal progress (learning path, coaching, evidence), handoffs (support, partnership, agency), feedback, and team summaries. No obvious gaps for the stated purpose.

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