AI Design Blueprint

Get Example

examples.get

Read-onlyIdempotent

Get one curated example by stable slug. Returns title, summary, source-code links, principle coverage (the principle slugs the example demonstrates), difficulty, library/framework, and implementation notes. Use this when you already have the slug from examples.search, a principles.get response, or a guide cross-link; prefer examples.search when filtering by topic / principle / difficulty / library; prefer guides.get when the caller wants a full walkthrough rather than a single reference example. Returns error_payload on unknown slug. Some entries are first-party agentic patterns (entry_kind='pattern') rather than upstream cookbook examples: those additionally return pattern_slug, pattern_family, when_to_use, doctrine_relations (each {principle_id, relation, note, code_ref} where relation is one of structural / default_gap / depends), prior_art, and doctrine_binding_basis. Every other row omits those seven keys.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	Yes	Stable slug of the curated example (e.g. 'agents-building-blocks-5-control').

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?

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses error behavior ('Returns error_payload on unknown slug') and explains the two possible entry kinds, including the conditional additional fields for pattern entries. This adds significant context not captured in annotations.

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 front-loaded with the core action and return types, then uses conditional clauses to explain variants. Despite length, every sentence contributes necessary information; no fluff or repetition.

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?

For a tool with one parameter and an output schema, the description thoroughly explains return values, error handling, and edge-case entry variants. It also positions the tool within the broader toolset, making it complete for an agent deciding whether to invoke it.

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 only parameter, 'slug', is fully described in the input schema (100% coverage). The tool description does not add new semantic details beyond using the term 'stable slug', which already appears in the schema. Baseline 3 is appropriate since schema handles the parameter meaning.

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?

Purpose is explicitly stated: 'Get one curated example by stable slug.' It names the verb and resource, and distinguishes from siblings by contrasting with examples.search and guides.get (e.g., 'prefer examples.search when filtering ... prefer guides.get when the caller wants a full walkthrough').

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: 'Use this when you already have the slug from examples.search, a principles.get response, or a guide cross-link', and names alternatives with conditions: 'prefer examples.search when filtering by topic / principle / difficulty / library; prefer guides.get when the caller wants a full walkthrough'.

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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