AI Design Blueprint

Submit Feedback

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

A4.4/5.0

Behavior5/5

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

The description discloses write-only behavior, no auth required, a single insert into UserFeedback, UK/EU residency, and the privacy boundary for contact_email. These traits go beyond the sparse annotations (all false hints) and inform the agent about side effects and constraints.

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 well-structured with clear labeled sections (WHEN TO CALL, WHEN NOT TO CALL, BEHAVIOR) and front-loaded purpose. It is longer than necessary and repeats the contact_email privacy condition twice, but every section carries useful guidance, so minor redundancy prevents a perfect score.

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?

Given the complexity of 9 optional parameters and an output schema, the description covers when to call, behavior, privacy, and regulatory context. It does not need to explain return values because an output schema exists. This is a complete and appropriately rich description for a feedback tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents all 9 parameters at 100% coverage, so baseline is 3. However, the description references 'category' and 'free-text body' which do not match any schema field, creating potential confusion. It repeats the contact_email privacy rule already present in the schema, adding no new parameter value.

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 it records explicit free-text user feedback about the Blueprint, tool surface, or a principle/example. It uses a specific verb and resource, and immediately distinguishes itself from signals.report, a sibling tool for metrics. The purpose is unambiguous and well-scoped.

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 'WHEN TO CALL' section is explicit with examples, and the 'WHEN NOT TO CALL' section clearly prohibits proactive or silent invocation and substituting for signals.report. It names the alternative tool and provides concrete guidance, which is exactly what an agent needs to select correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All AI Design Blueprint tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose within its domain: the validators are differentiated by lens (architect/design/spec), content tools are split by entity (principles/clusters/guides/examples/assets) with list/get/search variants, and even the me.* and handoffs.* tools have non-overlapping functions. The only near-overlap (architect.validate vs architect.validate_consensus) is explicitly disambiguated by the consensus variant's description.

Naming Consistency4/5

Tool names consistently use a domain prefix (architect., principles., me., etc.) and snake_case throughout. While most are action-oriented (validate, list, get, search, add, await, report, summarize), some me.* and handoffs.* names are noun phrases (me.learning_path, handoffs.agency) that don't signal the action as clearly, creating minor deviation from a pure verb_noun or action pattern.

Tool Count3/5

At 29 tools, the set is heavy but justified by the server's broad multi-domain scope (doctrine, validation, learning, support, and team analytics). Each tool has a distinct role, but the number exceeds the typical well-scoped range, and some content types (e.g., examples) could have been consolidated without losing function.

Completeness4/5

The server covers its apparent domains thoroughly: doctrine content has list/get/search for most entity types, validation covers architecture/design/spec with consensus and certification, and user learning/support have appropriate tools. Minor gaps exist—e.g., examples have no list-all endpoint, and session management is web-only—but none are blocking for core workflows.

Resources

Need Help?