AI Design Blueprint

Search Principles

principles.search

Read-onlyIdempotent

Search Blueprint principles by free-text query and return the closest matches ranked by relevance. Use this to find principles related to a specific design challenge, failure mode, or keyword (e.g. 'reversibility', 'approval flow', 'delegation boundary'). Returns principle title, cluster, definition, rationale, and implementation heuristics. Prefer this over principles.list when you have a specific topic in mind rather than wanting all principles. NOTE: search currently covers the 10 agentic principles only; for the 8 experience-design laws or the 8 spec-quality laws use principles.list(lens='surface') / principles.list(lens='spec') until search spans all three lenses.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of results to return. Capped at server maximum.
`query`	Yes	Free-text search query matched against principle title, definition, rationale, and cluster.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

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

The annotations already declare read-only and idempotent behavior; the description adds crucial behavioral context: the current scope limitation to 10 agentic principles and the exact alternative calls for the other lenses. It also discloses the return fields and ranking behavior.

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?

The description is appropriately sized, front-loaded with the main purpose, and every sentence contributes value: function, examples, alternative guidance, and a scope note. No wasted words.

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 rich schema, output schema, and annotations, the description is complete: it explains what the tool does, when to use it, what it returns, and its current limitations. It fully equips an agent to select and invoke the tool correctly.

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?

The input schema already provides 100% coverage for both parameters, including descriptions of what the query matches and limit behavior. The description reinforces the query semantics with examples but does not add essential meaning beyond the schema.

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 states a specific verb ('search'), resource ('Blueprint principles'), and behavior ('return the closest matches ranked by relevance'). It also explicitly distinguishes itself from principles.list, making the tool's unique purpose clear.

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-use guidance: 'Prefer this over principles.list when you have a specific topic in mind', and also gives a when-not-to-use with specific alternatives for other lenses. Concrete examples of queries enrich the guidance.

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

A4.4/5.0

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

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