AI Design Blueprint Doctrine

Search Examples

examples.search

Read-onlyIdempotent

Search curated examples by free-text query, ranked by relevance, with optional filters: principle_ids (only examples covering those principles), difficulty (beginner/intermediate/advanced), library (e.g. 'langgraph', 'openai'). Returns each match's slug, title, summary, principle coverage, difficulty, library, and source-code link — slug is the handle examples.get hydrates. Default limit 5, capped server-side. Use this when the user describes a use case, technique, or library and wants matching examples; prefer examples.get when you already have the slug; prefer guides.search when the user wants a full walkthrough; prefer principles.search when the user wants doctrine guidance, not an implementation. Results may include first-party agentic patterns (entry_kind='pattern') carrying an explicit doctrine binding, see examples.get. Filter to one family with pattern_family, which implies patterns only. Patterns take a small relevance preference over generic examples when otherwise equally relevant; that preference never outranks a genuine failing-principle match, and a pattern whose only relation to a failing principle is 'depends' receives no such match at all.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum number of results to return. Capped at server maximum.
`query`	Yes	Free-text search query matched against example title, summary, and metadata.
`library`	No	Filter by library or framework name (e.g. 'langgraph', 'openai', 'anthropic').
`difficulty`	No	Filter by difficulty level.
`principle_ids`	No	Filter to examples that cover these principle IDs.
`pattern_family`	No	Filter to one agentic-pattern family. Implies patterns only, since no upstream cookbook example carries a family.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already provide readOnlyHint=true and idempotentHint=true. The description adds behavioral context such as 'Default limit 5, capped server-side', inclusion of first-party agentic patterns, and relevance preference rules for patterns. No contradictions 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 well-structured and front-loaded with purpose. It is somewhat lengthy due to detailed behavioral rules (pattern relevance, filter implications), but every sentence adds value. Could be slightly trimmed without losing clarity, but overall appropriately concise for the 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?

Given the tool's complexity (filters, pattern families, relevance preferences, relationship with examples.get), the description covers all necessary aspects: return fields (slug, title, summary, etc.), limit behavior, filter implications, and guidance on when to use sibling tools. Output schema handles return format details.

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 description coverage is 100%, so baseline is 3. The description mentions parameters like principle_ids, difficulty, library, and pattern_family, but the schema already provides descriptions for each. The description does not add significant new meaning beyond what is in the schema, maintaining the 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 'Search curated examples by free-text query, ranked by relevance' with specific filters, distinguishing it from siblings like examples.get, guides.search, and principles.search. The verb 'search' and resource 'curated examples' are explicitly defined, and the distinction from sibling tools is made through comparative statements.

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 description explicitly states when to use this tool ('when the user describes a use case, technique, or library and wants matching examples') and when to prefer alternatives (e.g., 'prefer examples.get when you already have the slug; prefer guides.search when the user wants a full walkthrough; prefer principles.search when the user wants doctrine guidance'). It also explains the behavior for pattern relevance and filter implications.

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 Doctrine 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

A4.6/5.0

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.

Resources

Need Help?