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aidesignblueprint

AI Design Blueprint Doctrine

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signals.report

Record value moments after successful validation or design sessions by capturing event type, context, and user feedback to track AI design session impact.

Instructions

Pro/Teams — records a value moment after a successful validation or design session. Only structured fields stored — no prompts or code. Do not include proprietary content in brief_context. Offer once per session after a clear success signal; never silently. Requires active Pro or Teams Bearer token. Auth: Bearer .

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
event_typeYesPick the type that best matches what just happened: 'review_confidence' — architect.validate returned aligned; 'runtime_risk_found' — architect.validate found violations; 'workflow_clarity' — principles/examples clarified a design decision; 'agent_setup_success' — user successfully wired up an agent or MCP tool; 'onboarding_helped' — user understood how to start using the Blueprint; 'research_time_saved' — user found relevant doctrine faster than expected; 'team_alignment' — Blueprint helped align a team on agentic design; 'other' — use only if none of the above fit.
surface_usedNoWhere the value was experienced. Use 'mcp' when called from Claude Code, Cursor, Windsurf, or any MCP client. Use 'principles' if the user was browsing or searching principles. Use 'examples' if the user was reading implementation examples. Use 'for-agents' if the user came via the /for-agents page. Use 'learn' or 'certification' for course-related sessions.
brief_contextNo1–2 plain-English sentences summarising what was helpful. Example: 'Validation identified a missing approval gate before email send.' No code snippets, no proprietary content, no user PII. Max 500 chars.
perceived_valueNoAsk the user: 'On a scale of 1–5, how valuable was this session?' Map their answer directly: 1=low, 5=high. Do not guess — only set this if the user gave an explicit score.
workflow_stageNoInfer from what the user was doing: 'exploring' — reading doctrine, browsing principles; 'designing' — planning architecture or agent flows; 'implementing' — writing or refactoring code; 'reviewing' — running architect.validate on existing code; 'shipping' — preparing for production or deployment.
would_recommendNoAsk the user: 'Would you recommend the Blueprint to a colleague?' Set true/false based on their answer. Only set if asked — do not assume.
team_sizeNoIf the user mentions their team size during the session, record it here. Do not ask for it explicitly — only capture if volunteered.
Behavior4/5

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

Annotations show this is a write operation (readOnlyHint=false, idempotentHint=false), and the description confirms it 'records' data. It adds useful behavioral context: only structured fields stored, auth requirements (active Pro/Teams Bearer token), and privacy constraints (no code, no PII). No contradiction with annotations.

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?

The description is concise (4 sentences) and front-loaded with the core purpose. The inclusion of 'Pro/Teams' is mildly extraneous but acceptable for context. Overall well-structured with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 7 parameters (1 required), no output schema, and rich schema descriptions, the description covers usage context, auth, and behavioral constraints adequately. It explains when to use and key restrictions, but could benefit from mentioning the return value (e.g., success confirmation) since no output schema exists.

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% with detailed parameter descriptions in the input schema. The description adds little beyond stating 'Only structured fields stored', which aligns with schema but does not enhance parameter understanding. Baseline 3 is 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?

The description clearly states the tool records a 'value moment after a successful validation or design session', distinguishing it from sibling signals.feedback by focusing on positive outcomes. The verb 'records' and resource 'value moment' are specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Offer once per session after a clear success signal; never silently', providing clear when-to-use guidance. It also warns against including proprietary content in brief_context. However, it does not mention the sibling signals.feedback as an alternative for negative feedback.

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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