AI Design Blueprint

My Validation History (architecture + design + spec)

me.validation_history

Read-onlyIdempotent

Pro/Teams — return the authenticated user's validation run history for all three lenses (architect.validate → validator='architecture', design.validate → validator='surface', spec.validate → validator='spec') with the Blueprint Readiness Score (0-100), letter grade (A-F), and tier (draft, emerging, production_ready). Each run carries a validator field naming its lens. Three lookup modes: (1) run_id=<id> returns a SINGLE run with the full persisted result_json — use this to RECOVER a result when your MCP client tool-call timed out before architect.validate, design.validate, or spec.validate returned. The run completes server-side and persists; the run_id is surfaced in the first progress notification of every validate call so you have the recovery handle even when your client gives up early. (2) repository=<name> returns the full per-run trend for that repository plus a regression diff between the latest two runs. (3) No arguments returns one summary per repository the user has validated, sorted by most recent. Use modes (2) or (3) BEFORE re-validating the same repository on either lens — they tell you which principles or laws regressed since the last run, so you can focus the new review on what is actually changing. Auth: Bearer . Pro or Teams plan required.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum number of runs to return when scoped to a single repository. Capped at 50. Ignored when `run_id` is provided.
`run_id`	No	Single-run lookup by run_id (UUID). Returns the persisted result_json verbatim — the same payload architect.validate would have returned if your client hadn't timed out. Use this to recover a result when your MCP tool-call closed before the server returned. Per-run authorisation: returns only runs owned by the calling user.
`repository`	No	Repository name or path to scope the history to. Pass the same value you would pass to architect.validate. Omit to get one summary per repository. Mutually exclusive with `run_id` — if both are passed, `run_id` wins.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

The description adds significant behavioral detail beyond annotations: runs complete server-side, run_id is surfaced in the first progress notification, per-run authorization limits to owned runs, regression diff behavior, and sorting. No contradiction with the readOnly/idempotent hints.

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 length justified given the tool's three modes and use cases. It is front-loaded with a clear purpose statement, then structures the modes with numbered lists for easy scanning. No redundant or filler sentences.

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?

The description fully covers a complex tool: all modes, recovery workflow, regression analysis, auth, and plan requirements. Since an output schema exists, return-value details are not needed. This is complete for an agent to select and invoke correctly.

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

Parameters4/5

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

Schema coverage is 100% with descriptive parameter texts, so baseline is 3. The description adds value by contextualizing parameters into the three lookup modes and linking run_id to timeout recovery and repository to regression diffs, going beyond mere schema restatement.

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 returns the authenticated user's validation run history for all three lenses, with a specific verb ('return') and resource. It distinguishes itself from sibling validate tools by specifying it is a history/retrieval tool, including the exact validator values for each lens.

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?

Explicit guidance is provided: use run_id to recover results after a client timeout, and use repository/no-arg modes before re-validating to identify regressions. It also notes Auth requirements and plan limitations, giving clear conditions for appropriate use.

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.

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