AI Design Blueprint Doctrine
Server Details
The industry standard reference for safe, observable, and steerable AI agent UX. Browse and search the 10 Blueprint principles, principle clusters, curated implementation examples, and application guides. 13 public tools require no credentials. Tools for learning path, coaching context, and handoffs require a Firebase Bearer token. Validation and usage summary tools require a Pro or Teams membership.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4/5 across 22 of 22 tools scored. Lowest: 3.3/5.
Each tool has a clearly distinct purpose within its category. Descriptions explicitly contrast related tools (e.g., 'Prefer clusters.list when you need to discover which clusters exist' vs. 'Use clusters.get when you already know the exact cluster slug'), eliminating ambiguity.
All tool names follow the consistent pattern `<category>.<action>` using lowercase snake_case (e.g., `architect.validate`, `clusters.list`, `guides.search`). No mixing of styles or unpredictable verbs.
22 tools is slightly above the typical 10-15 range but still well-scoped for the domain's complexity. Each category (validation, discovery, handoffs, personal progress, signals) earns its tool count.
The tool set covers the full lifecycle: discovery (principles, clusters, examples, guides), validation (architect.validate, validation_history), personal tracking (me.*), human handoffs (handoffs.*), feedback (signals.*), and team features (team.summarize). No obvious gaps.
Available Tools
24 toolsarchitect.certifyCertify Production-Ready ArchitectureAInspect
Pro/Teams — second-pass adversarial certification of an architect.validate run that scored production_ready (A or B first-pass tier). ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. RECOVERY FIRST: the run_id is emitted in the FIRST notifications/progress event at t=0s (BEFORE the LLM call begins). Capture it. On timeout, call me.validation_history(run_id='<that-id>') to fetch the persisted cert verdict; the server-side run completes independently within a 20-minute budget. This is the canonical recovery path. Use it before considering any retry. Long-running LLM call (60-180s typical; exceeds Claude Code's ~60s idle budget); MCP clients commonly close the call before the server returns. Retrying re-runs the LLM call AND burns one of your 3 cert retry-budget attempts. Mints the certified production_ready badge when both reviewers sign off; caps the run to C/emerging when the second pass surfaces a missed production_blocker. MANDATORY DOCTRINE RULE (load-bearing): the badge certifies the EXACT code that produced the validate run_id, NOT 'this codebase' in general. If you modify, fix, or iterate the code between architect.validate and architect.certify — even a single character — cert rejects with code_fingerprint_mismatch. Fixing the code voids the run. The recovery path is always: edit code → architect.validate → fresh run_id → architect.certify on the fresh run. Do NOT cert from a stale run_id after iteration; ask the user to re-validate first. WHEN TO CALL: only after architect.validate returned tier=production_ready AND the user wants the certified badge AND the code has not been touched since the validate run. NOT for tier=draft/emerging/not_applicable runs (typed rejections fire — see below). NOT idempotent across attempts: each call is one of the 3 attempts in the retry budget. BEHAVIOR: atomic one-shot single LLM call, ~60-180s server-side at high reasoning effort (small payloads finish faster; observed p99 ~250s; server-side budget is 20 min, ~5× observed max). Exceeds typical MCP-client tool-call idle budget (~60s in Claude Code), so the FIRST notifications/progress event fires at t=0 carrying the run_id. The run is atomic by contract — no in_progress lifecycle, no cancellation, no resume. Updates the persisted run's result_json (public review URL + me.validation_history(run_id=...) reflect the cert outcome). ELIGIBILITY GATE (typed rejection enum on failure): caller must own the run, tier=production_ready, less than 24h old, not already certified, within cert retry budget (max 3 attempts), no other cert call in flight for the same run_id, code fingerprint must match the validated code, AND the submitted payload must be cert-payload-complete (see Payload Completeness below — cert rejects pre-LLM with payload_incomplete when an imported module's surface isn't visible in the validate payload that produced this run_id). Rejection reasons (typed Literal): auth_required, paid_plan_required, run_not_found, not_run_owner, not_eligible_tier, not_agentic_component (tier=not_applicable runs), already_certified, certification_age_exceeded, retry_budget_exhausted, code_fingerprint_mismatch, code_fingerprint_missing, code_not_on_file (caller omitted code argument AND the 24h cert-retry hold for this run has expired or was never written. Recovery: re-run architect.certify from the same MCP session that ran architect.validate, passing the code explicitly — the server never persists code by design), payload_incomplete (submitted/validated payload imports modules whose contents aren't visible — cert refuses pre-LLM to prevent a false-precision downgrade. Recovery: re-validate with verbatim public-surface stubs for every imported module, then re-cert on the fresh run_id. Empirically validated: PR #157 iter8/iter9 cert rejections were exactly this class — code on disk was correct, the submitted payload merely omitted module visibility), cert_consensus_score_below_threshold (consensus_median<75 — consensus runs only), cert_consensus_unstable_blocker (any principle mode_stability<80% — consensus runs only), run_state_corrupt, cert_persistence_failed, cert_in_flight (a prior architect.certify call on this run_id is still running. Poll me.validation_history for the verdict; do not retry until it resolves). PAYLOAD COMPLETENESS (load-bearing for cert eligibility): the cert reviewer reads the EXACT payload that produced the validate run_id. Imported modules whose surface isn't present in the payload cause pre-LLM payload_incomplete refusal. Avoidance — when validating with intent to cert, bundle public-surface stubs for every imported module: from sqlalchemy.exc import SQLAlchemyError → include a stub class; from app.db import models → include a class models: namespace stub with the columns/methods you reference; module-level imports of dataclass, Literal, json, datetime, timezone MUST also be in the payload (cert correctly catches when they're omitted — code would NameError on import). 'Submit Like Production': the payload should be the code as it would actually run, not a compressed sketch. PRE-LLM REJECTION AUDIT TRAIL: when cert rejects before the LLM call (payload_incomplete, code_fingerprint_mismatch, etc.), certification_attempts=[] on the response — no attempt landed in the retry budget, no LLM hop occurred. The rejection envelope's rejection_reason + guidance are the actionable surface. (Audit-trail UI surfacing of pre-LLM rejections is tracked in the platform self-audit set as anomaly #5; out of scope for the cert tool itself.) INPUTS: re-send the SAME code that produced the run_id (the architect persists findings + recommendations, never code, by design — privacy-preserving). Server compares the submitted code's SHA-256 fingerprint to the stored fingerprint and rejects mismatches. Auth: Bearer , Pro or Teams plan required. UK/EU data residency (Cloud Run europe-west2). Code processed transiently by OpenAI (no-training-on-API-data) and dropped; payloads JSON-escaped + delimited as inert untrusted data — prompt-injection inside code is ignored. If the cert call fails outright (provider error, persistence error), a fresh architect.certify is the recovery path; the eligibility gate enforces the 3-attempt retry budget. For long-running cert workflows the answer is to re-validate, not to make this tool stateful. OUTCOMES: certification_status ∈ {confirmed_production_ready (badge mints), downgraded_to_emerging (cert review surfaced a missed production_blocker, tier capped at C/emerging), unavailable_provider_error (LLM call failed, retry within budget)}. Cert findings + summary + attempt history surfaced on the persisted run for full inspectability.
| Name | Required | Description | Default |
|---|---|---|---|
| code | No | The same code that was sent to architect.validate to produce this run_id. Sent verbatim — the cert reviewer needs the actual code to surface production_blockers the first pass missed. May be omitted (empty string) when the prior validate stored the code under the 24h cert-retry hold; in that case the server reuses the stored code automatically. Sent under the same enterprise-safety envelope as architect.validate (transient processing, no training, JSON-escaped + delimited). | |
| run_id | Yes | The run_id from a prior architect.validate call. Returned in the validate response when persistence_status='saved'. Must be owned by the caller (per-user authorisation, same gate as me.validation_history). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint=false, idempotentHint=false, openWorldHint=true), the description reveals key behavioral traits: it is a synchronous one-shot call (~20-40s), atomic with no lifecycle, no cancellation, and fail recovery requires a fresh call. It also discloses enterprise-safety handling, data residency, and auth mechanism, fully informing the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but front-loaded with the core purpose and outcome. Every sentence adds value, covering eligibility, behavioral constraints, safety, and recovery. While slightly verbose, it is well-structured and efficient for the amount of necessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (certification workflow, eligibility, safety, no output schema), the description is remarkably complete. It covers failure recovery, retry budget, privacy-preserving design, data residency, and auth. An agent has all information needed to decide when and how to invoke this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverate with descriptions for both parameters (run_id and code). The tool description adds some extra context (e.g., code must be verbatim, run_id ownership), but the schema descriptions are already adequate. Therefore, the description adds marginal value beyond the schema, justifying a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('certify') and resource ('architect.validate run with production_ready tier'), distinguishing it clearly from the sibling tool architect.validate. It details the outcome (minting badge or capping to C/emerging), leaving no ambiguity about the tool's purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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: after a first-pass architect.validate that scored production_ready. It provides eligibility criteria (ownership, tier, age, not already certified, retry budget) and explains that long-running workflows should re-validate instead. However, it does not explicitly compare to other tools like handoffs or assets.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
architect.validateValidate Agent ArchitectureAInspect
Pro/Teams — first-pass doctrine review of agentic code/workflow against the 10-principle Agentic AI Blueprint. ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running LLM call (60-180s typical); MCP clients commonly close the call before the server returns. Retrying re-runs the 60-180s LLM call from scratch and burns compute. RECOVERY: the run_id is emitted in the FIRST notifications/progress event at t=0s (before the LLM call begins) — capture it. On timeout, call me.validation_history(run_id='<that-id>') to fetch the persisted result; the server-side run completes independently within a 20-minute budget. Edge case: if the transport dropped before the first progress notification (very rare; sub-second window), call me.validation_history(repository='<same value you passed here>') to find your most recent run. TASK-AUGMENTED INVOCATION (MCP 2025-11-25, SEP-1686): clients that advertise the tasks capability can task-augment this call by including task: {ttl: <ms>} inside the JSON-RPC request's params (NOT as a tool argument; alongside arguments, _meta, etc.). The server returns a CreateTaskResult immediately (taskId equals the run_id above) and runs the validation in the background. Spec-correct long-running pattern: poll via tasks/get for state, fetch the terminal payload via tasks/result, listen for notifications/tasks/status for push updates, and cancel via tasks/cancel. _meta.progressToken from the original request stays valid for the entire task lifetime. Sync (non-augmented) calls behave exactly as before, backwards-compatible by construction. The me.validation_history(run_id=...) recovery path remains the canonical recovery handle for clients that don't yet advertise the tasks capability. Returns code_classification (autonomous_agentic_workflow vs non_agentic_component), per-principle findings (verdict, severity_score 0-100, severity_class, code-cited evidence, recommendation), severity-weighted readiness (score|null, grade|null, tier ∈ {production_ready, emerging, draft, not_applicable}), recommended examples, reproducibility envelope (model, seed, doctrine_fingerprint, prompt_template_fingerprint), persistence_status with shareable run_id/badge_url/review_url. WHEN TO CALL: the user wants a governance audit, readiness score, or production_ready badge on an agent/workflow they just built or changed. WHEN NOT TO CALL: non-agentic plumbing (math utilities, type aliases, event-loop helpers, single-shot request/response handlers) returns tier=not_applicable with score=null/grade=null — that's not a failure, the doctrine simply doesn't grade non-agentic code, and architect.certify will refuse with not_agentic_component. Submit the OWNING agentic workflow instead. BEHAVIOR: long-running LLM call (~60-180s typical at high reasoning effort, single-pass; server-side budget 20 min). Mints run_id at t=0; first notifications/progress event carries run_id as recovery handle; keepalive every 30s. Persists ValidationRun + UserValidationRun + AIValidationRunLog + LLMUsageLog atomically; on rollback, badge/review URLs are stripped. Auth: Bearer , Pro/Teams plan. UK/EU residency; transient OpenAI processing (no-training); prompt-injection in code is inert. INPUTS: send FULL file contents verbatim as implementation_context (NO truncation, NO ... placeholders, NO comment removal — the architect treats your ... as literal code and hallucinates bugs that don't exist). If too large, split into MULTIPLE calls scoped by file/module; never truncate one call. Pass repository="" to group runs into a project trend. Pass private_session=true to bypass server-side logging (persistence + recovery disabled). focus_area narrows scope; unmatched focus_area fails explicitly rather than silently widening. PAYLOAD COMPLETENESS (load-bearing if you intend to architect.certify this run): the validate first-pass is permissive — it scores on doctrine alignment + structural patterns visible in the submitted code. Cert's adversarial second-pass is rigorous — it scores on cert-payload-completeness as well as code correctness. A run that scores 100/A at validate can cert-reject pre-LLM with payload_incomplete when imported modules' surfaces aren't visible. To validate with INTENT TO CERT, also bundle verbatim public-surface stubs for every imported module: from sqlalchemy.exc import SQLAlchemyError → include a stub class; from app.db import models → include a class models: namespace stub with the columns/methods the code references; module-level imports of dataclass, Literal, json, datetime, timezone MUST also be in the payload (cert correctly catches when they're omitted — the module would NameError on import as submitted). 'Submit Like Production': the payload should be the code as it would actually run. Empirically reconfirmed PR #157 iter8 → iter9 cert downgrades. SCORE VARIANCE DISCLOSURE (anomaly #10 — empirically documented): validate scores are POINT ESTIMATES with an observed empirical variance band of ~20-67 pts on BYTE-IDENTICAL input. Runs against the same repository, same code, same deterministic seed (the seed is derived from input — same input → same seed) can produce materially different scores AND different top-blocker rankings, because OpenAI's reasoning models at reasoning_effort=high are not strictly deterministic even with the seed parameter pinned. The reproducibility_mode='best_effort' field on every response is the platform's honest disclosure of this property. For decisions where stability matters more than speed, call architect.validate_consensus (N=3-5 aggregated, median verdict + per-principle stability metrics) instead — collapses the variance, surfaces unstable principles explicitly. A single validate run is a single roll; consensus is the right tool when one score isn't enough. ITERATION LOOP — repository keying. Pass the SAME repository value across calls to chain iteration rounds; the validator auto-resolves the most recent prior run on (user, repository, scope) as prior_run_baseline and the LLM grades the new submission with iteration context (per-principle severity deltas surface in the response). Changing the repository string between calls — even subtly with an iter-2 suffix — silently severs the chain and yields a fresh blind first-shot. Round numbering belongs in task or commit messages, never in repository. See the architect-validation-orchestration skill in the agent-asset pack for the full validate → consensus → certify sequence. VERIFICATION LAYERS (the two-layer doctrine this platform practices on itself): validate verifies DOCTRINE ALIGNMENT against the 10-principle Blueprint — design patterns, hand-off explicitness, operational-state inspectability, race/blocker handling at the architectural level. validate does NOT guarantee runtime correctness. cert verifies PAYLOAD COMPLETENESS and runs an adversarial second pass over the submitted code — catches production_blockers the first pass missed, name-errors on import, missing module surfaces, etc. cert does NOT verify runtime correctness either. Passing validate is a NECESSARY condition for production_ready, not a sufficient one. Runtime correctness (does this actually execute and behave?) is verified at the THIRD layer — your tests, types, walks. The platform's own recursive-integrity practice: every PR runs validate against its own primitives, then cert. Real bugs surfaced via this practice in PR #157 — NULL-UUID false-positive (iter3) and tie-breaker mismatch (iter5) — that 25 unit tests had missed. Two-layer verification is the discipline, not 'either/or'. TYPED FAILURES: timed_out, rate_limited, dependency_unavailable, schema_mismatch (each carries retryable + next_action). NEXT STEP: if tier=production_ready (A or B grade), the response carries certification_status='not_evaluated' — call architect.certify(run_id, code) to mint the certified production_ready badge (separate ~60-150s adversarial review, eligibility-gated). See Payload Completeness above for the common pre-cert pitfall.
| Name | Required | Description | Default |
|---|---|---|---|
| task | No | What the agent or workflow is trying to accomplish. Adds evaluation context. | |
| files | No | List of file paths relevant to the implementation context. | |
| goals | No | Specific safety or quality goals to evaluate against (e.g. 'prevent irreversible actions', 'explicit approvals'). | |
| language | No | Programming language of the code being evaluated (e.g. 'python', 'typescript'). | |
| focus_area | No | Narrow the evaluation to a specific principle cluster or slug (e.g. 'delegation', 'visibility', 'establish-trust-through-inspectability'). | |
| repository | No | Iteration key. SAME value across calls auto-resolves the most recent prior run as `prior_run_baseline` for iteration-aware grading (per-principle severity deltas, regressions/improvements). CHANGING the value (even subtly with an `iter-2` suffix) silently severs the chain and yields a fresh blind first-shot. Round numbering belongs in `task`, not here. Empirical evidence of why anchoring matters: PR #157 iter1 33/F vs iter2 100/A on byte-identical baseline-race primitives (+67 spread); invoice-payment-manager #158 38/F vs #159 74/C (+36 spread) — same code, score variance from non-deterministic LLM at reasoning_effort=high; the baseline anchor collapses this onto a stable arc. | |
| example_limit | No | Maximum number of curated examples to include in recommendations. | |
| private_session | No | Set to true to disable logging AND prior-run anchoring AND run_id recovery for this call. Use for private one-shots that don't participate in the iteration arc. Default false. | |
| implementation_context | Yes | The artifact under review. SEND FULL FILE CONTENTS VERBATIM — the architect cites per-line evidence (identifiers, branch ordering, structural choices); any compression destroys evidence and produces hallucinated findings on code that isn't there. CONCRETE DON'TS: do NOT replace docstrings/comments with `...`; do NOT condense multi-line statements; do NOT replace dict/set comprehensions with `{...}`; do NOT remove explanatory comments to save tokens. If the file is large, split into MULTIPLE architect.validate calls scoped by file/module — never truncate one call. Architecture summaries (high-level prose) accepted ONLY for greenfield (no code yet); never as a substitute for code that already exists. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that data is sent to Anthropic and only structured results are stored, and that private_session disables logging. Annotations already indicate mutability (readOnlyHint=false), so the description adds valuable privacy context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, each serving a purpose: purpose and outputs, data handling, and authentication. No fluff, highly focused.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present and 9 parameters, the description covers the core functionality, privacy implications, and auth requirement. It is sufficient for an agent to understand when and how to use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add significant parameter semantics beyond what the schema already provides, though it clarifies the private_session behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool evaluates code, workflows, or architecture against Blueprint doctrine and returns principle coverage, findings, and recommendations. This is specific and distinct from sibling tools like assets.list or clusters.get.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for architecture validation but does not explicitly state when to use versus alternatives or provide conditions for non-use. It mentions 'Pro/Teams' which hints at plan restrictions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
architect.validate_consensusValidate Agent Architecture (Consensus Mode)AInspect
Pro/Teams — N-shot CONSENSUS doctrine review of agentic code. ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running (~80-120s for N=3 parallel LLM calls); MCP clients often close the call before the server returns. Retrying re-runs N × 60-180s LLM calls from scratch and burns N× compute. RECOVERY: same heartbeat pattern as architect.validate — the run_id is emitted in the FIRST progress event at t=0s (before LLM children fire); on timeout, call me.validation_history(run_id='<that-id>') to fetch the persisted consensus envelope. Runs N parallel architect.validate calls with private_session=True, then aggregates them to a per-principle MODE verdict + median severity + per-principle stability + score range/stdev. Returns one ConsensusValidationResponse with the headline median score, the honest variance band, and a representative full ValidationResponse (the child whose score is closest to the median). WHEN TO CALL: the user wants an HONEST first-pass score on agentic code, with the architect's variance surfaced. The single-shot architect.validate re-asserts the prior persisted run's verdict via baseline-anchor injection — same code can score 60/C anchored vs 98/A unanchored. Consensus mode is the unanchored honest read. WHEN NOT TO CALL: when you NEED the iteration delta against a prior run (regressions/improvements panel) — for that, call architect.validate which keeps baseline injection on. CHAIN RESUME: each child runs with private_session=True (no anchor) on purpose, but the CONSOLIDATED outer row IS persisted with lifecycle_status='completed' — the next single-shot architect.validate on the same repository auto-resolves it as prior_run_baseline. Consensus checkpoint becomes the new anchor. See the architect-validation-orchestration skill in the agent-asset pack for the full validate → consensus → certify sequence. BEHAVIOR: N (default 3, max 5) parallel LLM calls run concurrently; wallclock ~80-120s for N=3 (max child latency, not sum). Cost = N × LLM bill. Each child runs with private_session=True so the doctrine prompt's prior-run baseline injection is suppressed (no anchor bias). One CONSOLIDATED UserValidationRun row is written carrying the consensus envelope; the N children themselves do NOT persist (private_session contract). AUTH: Bearer , Pro/Teams plan. Same paid-plan gate as architect.validate. INPUTS: same shape as architect.validate. n is the only extra arg (range 2..5). private_session is implicit (always true for children); the OUTER consolidated row IS persisted unless the tool itself is called inside another private context — but no such wrapper exists today. OUTPUT: response carries score_consensus_median (headline), score_stdev (honest uncertainty), score_range (min, max), mode_stability_min_pct (the cert-eligibility gate's input — ≥ 80% means the consensus is stable), per_principle (mode + distribution + severity median per principle), and representative_response (the closest-to-median child's full ValidationResponse so existing UI components render unchanged). TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable). Plus consensus-specific: consensus_quorum_failed when fewer than 2 child runs succeeded (≥ 2 required to compute a meaningful median).
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | Number of parallel child runs. Default 3 (the variance signal is visible at N=3; cost = 3× LLM bill). Capped server-side by Settings.consensus_n_max (default 5). | |
| task | No | What the agent or workflow is trying to accomplish. | |
| files | No | List of file paths relevant to the implementation. | |
| goals | No | Specific safety or quality goals to evaluate against. | |
| language | No | Programming language of the code (e.g. 'python'). | |
| focus_area | No | Optional: narrow the review to a principle cluster or slug. | |
| repository | No | Iteration key. Consensus children all run unanchored (`private_session=True`), but the consolidated row IS persisted under this key — discoverable as prior baseline for the next single-shot `architect.validate`. Same value across calls keeps the iteration arc inspectable. | |
| example_limit | No | Max curated examples per child run. | |
| implementation_context | Yes | The artifact under review. SEND FULL FILE CONTENTS VERBATIM — same constraint as architect.validate. Truncation produces hallucinated findings on code that isn't there. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses parallel execution, cost (N x LLM bill), wallclock time (80-120s for N=3), recovery pattern (heartbeat with run_id), typed failures, and the persistence behavior of children vs consolidated row. This far exceeds the annotation signals (readOnlyHint=false, etc.) and provides deep behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but well-structured with clear sections (behavior, when to call, output, recovery, failures). Every part adds value, but some redundancy exists (e.g., repeating plan requirements). It is front-loaded with the most critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description thoroughly explains the return values (score_consensus_median, stdev, per_principle, etc.) and covers all aspects needed for an agent to use the tool correctly: purpose, usage, behavior, recovery, and failure modes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema (e.g., capping n server-side, reiterating the implementation_context constraint), but does not significantly enhance parameter understanding. The schema itself is already detailed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs an N-shot consensus review of agentic code by running parallel 'architect.validate' calls and aggregating results. It distinguishes itself from the sibling 'architect.validate' by explaining the difference in anchor bias, making the purpose specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to call ('user wants an honest first-pass score') and when not to call ('when you need iteration delta against a prior run'), even naming the alternative tool. This provides clear context for agent selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
assets.listList Agent AssetsARead-onlyIdempotentInspect
Public — list downloadable doctrine and agent asset artifacts (skill packs, rule packs, MCP setup snippets) the user can drop into their AI coding tool to import the Blueprint as native skill/rule files. Returns a list of assets with name, format (one of: zip / md / markdown / mdc / json / toml / text — the full vocabulary), pack_version, download_url, and platform target (Claude Code, Cursor, Codex, Gemini, Qwen). The response also carries count (length of assets) for symmetry with principles.list / clusters.list / guides.list. WHEN TO CALL: the user asks how to bring the Blueprint into their coding agent, or wants to install it as a local skill/rule file. WHEN NOT TO CALL: for the live MCP tools themselves — those are already available through this server. For doctrine content, prefer principles.list/get and guides.list/get. BEHAVIOR: read-only, idempotent, no auth required. Asset artefacts are regenerated on every deploy from the canonical doctrine.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly and idempotent behavior. The description adds the 'downloadable' qualifier, hinting at the nature of the artifacts but no further behavioral traits. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is perfectly concise with no wasted words. All content is front-loaded and earned.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, annotations present, and an output schema existing, the description is mostly complete. However, it does not clarify what fields or format the list includes, which would be helpful for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so schema coverage is 100% by default. The description does not need to add parameter info; baseline for 0 params is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' and clearly identifies the resource as 'downloadable doctrine and agent asset artifacts', distinguishing it from sibling list tools like clusters.list, guides.list, and principles.list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as clusters.list, guides.list, or principles.list. The description does not mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
clusters.getGet ClusterARead-onlyIdempotentInspect
Get one principle cluster by stable slug. Returns the cluster definition, shared rationale, and the full set of member principles (slug + title) so the caller can pivot into principles.get without a second list call. WHEN TO CALL: the user has already named a specific cluster (e.g. 'delegation', 'visibility', 'trust', 'orchestration') OR you have a slug from a prior clusters.list / principles.list response and need its full definition + member principles. The response embeds member principle slugs + titles already, so DO NOT loop principles.get over each member to get a cluster overview — read the response. WHEN NOT TO CALL: the user is describing a topic, failure mode, or keyword in natural language (call principles.search instead); the user wants to discover which clusters exist (call clusters.list); the user wants the definition of one specific principle (call principles.get directly). Idempotent + cacheable per slug. Returns 404-shaped error_payload on unknown slug — the slug must match exactly the value emitted by clusters.list, with no normalization.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Stable slug of the principle cluster (e.g. 'delegation', 'visibility', 'trust', 'orchestration'). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint as true, so the description does not need to repeat. However, it adds no further behavioral context beyond stating the action, which is adequate given the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no unnecessary words or repetition, efficiently conveying the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval with one parameter and full annotations, the description is mostly complete. It could optionally mention that the response contains full cluster details, but the output schema handles that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers the single parameter fully (100% coverage), and the description only provides an example value ('delegation-and-scope') without adding new semantic meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get'), the resource ('principle cluster'), and the identifier ('stable slug'). It distinguishes itself from sibling tools like 'clusters.list' and 'principles.get' by specifying a single cluster retrieval by slug.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives (e.g., 'clusters.list'). While the purpose is clear, an agent would benefit from knowing not to use this for listing or filtering.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
clusters.listList ClustersARead-onlyIdempotentInspect
List all principle clusters with their stable slugs and linked principle titles. Use this to discover which clusters exist before drilling in with clusters.get or filtering principles.list by cluster. Prefer clusters.get when you already know the cluster slug and need full detail.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, which the description does not contradict. The description adds that clusters have 'stable slugs and linked principle titles,' but it does not disclose additional behavioral traits (e.g., sorting, pagination, rate limits). The description is consistent 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is directly relevant and front-loaded. Every word earns its place, with no extraneous content. It is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, has output schema), the description is complete. It tells the agent what the tool returns and the type of information included. Sibling tools exist but the description suffices for an agent to select and invoke this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters (100% schema coverage). The description does not describe parameters because none exist. It adds value by clarifying the output content (stable slugs and linked principle titles). Baseline 3 is appropriate, and since no param info is needed, a score of 4 reflects sufficient context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: listing principle clusters. It specifies what information is included (stable slugs, linked principle titles) and distinguishes it from sibling tools like clusters.get (which retrieves a single cluster) and principles.list (which lists principles).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description lacks any guidance on when to use this tool versus alternatives. For example, it does not mention that this tool returns all clusters without filtering, or when one might prefer to use clusters.get for a specific cluster. No usage context or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
examples.getGet ExampleARead-onlyIdempotentInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Stable slug of the curated example (e.g. 'agents-building-blocks-5-control'). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the description adds no further behavioral context. The description is consistent with annotations, but does not disclose additional traits like caching or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is front-loaded with the action and resource, containing no wasted words. Efficient and direct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one required parameter and an output schema. The description is adequate for a get-by-identifier tool, though it could briefly mention that the returned object includes example content. Still, it is complete enough for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the slug parameter already documented. The description adds 'stable slug' and a concrete example, but this adds minimal value beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'curated example', specifying the lookup method by 'stable slug'. This distinguishes it from sibling tools like 'examples.search' or 'examples.list'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives (e.g., 'examples.search' for query-based retrieval). However, the description implies usage for direct slug-based access, which is adequate for a simple get-by-identifier tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
examples.searchSearch ExamplesARead-onlyIdempotentInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description does not need to reiterate safety. It adds 'curated examples' but no further behavioral details (e.g., pagination, server caps). Not contradictory.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence with no filler. Every word adds value: verb, resource, and filtering dimensions. Perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, return values are covered. The description omits default limit (5) and query behavior, but these are in the schema. For a search tool with 5 parameters, the context is mostly sufficient; a minor addition about result ordering or cap could elevate it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description does not need to explain parameters. It mentions three parameter categories (principle coverage, difficulty, library) which align with schema fields, but adds no extra meaning or constraints beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search') and the resource ('curated examples'), listing filtering dimensions (text, principle coverage, difficulty, library). It distinguishes from sibling tools like 'examples.get' and 'guides.search' which retrieve specific items or search guides.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage via the verb 'Search' and filtering fields, but lacks explicit guidance on when to use this tool versus siblings like 'examples.get' or 'guides.search'. No exclusion or alternative tool names are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
guides.getGet Application GuideARead-onlyIdempotentInspect
Get a full application guide by its stable slug (e.g. 'security-application', 'observable-evaluation'). Returns sections, action items, and linked principles. Use this when you already have the guide slug from guides.list or guides.search. Prefer guides.search when the user describes a topic in natural language; prefer guides.list when you need the full inventory.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Stable slug of the application guide (e.g. 'security-application', 'observable-evaluation'). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and idempotentHint, so the description adds context by stating it returns a 'full application guide', which is consistent. However, it does not elaborate on any additional behavioral traits beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the purpose and provides an example. Every word is necessary and there is no superfluous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, output schema exists), the description is complete. It covers what the tool does, how to use it, and the expected input format. No gaps are evident.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description repeats the example slug format already in the schema's description, adding no new semantic value beyond the schema itself.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Get', the resource 'full application guide', and the identifier 'stable slug'. It distinguishes from sibling tools like guides.list or guides.search by specifying that it retrieves a single guide by its unique slug.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when you have a stable slug) but does not explicitly state when not to use it or mention alternatives. Given the presence of sibling tools, more guidance on choice would be beneficial.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
guides.listList Application GuidesARead-onlyIdempotentInspect
List application guides that show how Blueprint principles apply to engineering challenges (security, evaluation, observability, etc.). Use this to discover which guides exist before drilling in. Prefer guides.search when the user describes a topic or failure mode in natural language. Prefer guides.get when you already know the guide slug and need full detail.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is clear. The description adds behavioral context by specifying that the guides are about applying blueprint principles to specific challenges (security, evaluation), which provides useful semantic context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, action-initial sentence with no extraneous words. It directly states the verb and resource, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and an existing output schema, the description sufficiently explains what the tool returns (list of guides related to blueprint principles applied to engineering challenges). No further details are necessary for a simple list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema coverage is 100% and the description cannot add parameter-specific information. Baseline score of 4 is appropriate per guidelines.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action (list) and resource (application guides), and distinguishes it from siblings like guides.get and guides.search by specifying the content focus (blueprint principles applied to engineering challenges).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when one needs to list guides, but does not explicitly state when to use this tool versus alternatives like guides.get or guides.search, nor does it provide exclusions or prerequisites. Given the presence of siblings, some guidance would be beneficial.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
guides.searchSearch Application GuidesARead-onlyIdempotentInspect
Search application guides by free-text query, matched against section answers and action items. Use this when the user describes an engineering challenge (security review, evaluation harness, observability) and wants matching guides. Prefer guides.get when you already have the guide slug; prefer guides.list when you need the full inventory.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return. Capped at server maximum. | |
| query | Yes | Free-text search query matched against all guide content including section answers and action items. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint true. The description adds value by specifying what content is searched (section answers, action items), which goes beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with purpose and scope. No wasted words; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description adequately covers input semantics and search behavior. It is complete for a search tool with no missing critical information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds meaning about the query searching against specific guide content, which is not in the schema, thus providing additional semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it searches application guides by text query, and specifies it matches against all content including section answers and action items. This distinguishes it from sibling tools like guides.get (by ID) and guides.list (list all).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for free-text searching without explicit when-not or alternatives, but the context of sibling tools makes the use case clear. Lacks explicit exclusion statements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
handoffs.agencyRequest Agency HandoffBInspect
Authenticated — submit an agency engagement enquiry on behalf of the caller for a founder-led discovery call. Persists an AgencyHandoff row routed to the agency inbox; the user is contacted by the team for a scoped proposal. Engagement scopes: workflow sprint (rapid agentic workflow implementation), proof-of-concept (validate a specific agent design in a bounded timeframe), pilot support (co-design and validate a production-ready pilot), advisory (ongoing architectural guidance across a product team). WHEN TO CALL: the user has identified a paid hands-on expert engagement need beyond self-service learning, and explicitly asks to talk to the team or book a discovery call. ALWAYS confirm with the user before firing — this creates a sales-visible record. WHEN NOT TO CALL: for free training / partnerships discussion (use handoffs.partnership); for support / billing / access (use handoffs.operator); proactively or as a sales push. BEHAVIOR: write-only, single insert, side-effecting. Auth: Bearer (Firebase ID token, any plan). UK/EU residency. Response confirms the ticket id + scope so the user can reference it.
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Role or title of the person submitting the agency inquiry. | |
| locale | No | Response locale for the acknowledgment. | en |
| reason | Yes | Description of the engagement need: workflow sprint, proof-of-concept, pilot support, or advisory. | |
| company | No | Company or team name submitting the agency inquiry. | |
| website | No | Website or relevant URL for the team or project. | |
| agent_name | No | Name of the agent or client triggering the handoff. | mcp-client |
| support_type | No | Type of support needed. | |
| trace_summary | No | Optional agent trace summary for operator context. | |
| agent_platform | No | Platform or runtime the agent is running on. | |
| workflow_stage | No | Current workflow stage. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a write operation (readOnlyHint=false). The description adds authentication requirement ('Auth: Bearer <token>') but lacks details on side effects, confirmation, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose and essential auth info. No redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given an output schema exists (signal true), the description adequately covers the action. It could briefly note that trace_summary provides operator context, but schema handles that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters have descriptions in the schema (100% coverage), so the description adds no new meaning beyond listing engagement types which are already in the parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool submits an agency engagement enquiry for founder-led discovery calls, listing specific engagement types. However, it does not differentiate from sibling tools handoffs.operator and handoffs.partnership.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description only states what it does without any context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
handoffs.operatorRequest Operator HandoffAInspect
Authenticated — creates a support handoff record when an agent needs human review, account-specific escalation, or operator follow-up that cannot be resolved with the read-only doctrine tools. Persists a SupportHandoff row (reason, topic, page_url, agent_name, agent_platform, trace_summary, user_email) routed to the support inbox; user is contacted by the team. WHEN TO CALL: user explicitly asks for human help, hits a billing/access issue, or the agent has tried the doctrine tools and the user still needs a human. ALWAYS confirm with the user before firing — this creates a human-visible ticket. WHEN NOT TO CALL: proactively, silently, or to log debugging traces (use diagnostic logs instead); for partnerships/agency enquiries (use handoffs.partnership / handoffs.agency); for content questions answerable by principles.search / guides.search. BEHAVIOR: write-only, single insert, side-effecting (creates a ticket the team will see). Auth: Bearer (any plan). UK/EU residency. Response confirms ticket id + topic so the user can reference it.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | Topic category for routing (e.g. 'agent', 'billing', 'access', 'general'). | agent |
| locale | No | Response locale for the handoff acknowledgment. | en |
| reason | Yes | Clear description of why a human operator review is needed. | |
| page_url | No | URL of the page or context where the handoff was triggered. | |
| agent_name | No | Name of the agent or client triggering the handoff. | mcp-client |
| trace_summary | No | Optional summary of the agent's recent actions or trace for operator context. | |
| agent_platform | No | Platform or runtime the agent is running on (e.g. 'claude-code', 'cursor', 'copilot'). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only, non-idempotent, and open-world behavior. The description adds value by specifying the auth requirement (Bearer token), which is not present in annotations. This helps the agent understand the tool's invocation constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence stating the purpose, followed by one sentence on auth. It is front-loaded and every word earns its place. No unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, return values are covered. The description conveys the core purpose and auth requirement. However, given 7 parameters and no mention of side effects (e.g., notifications, processing details), some nuance is missing. Adequate but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema itself documents all 7 parameters. The description does not add additional semantics beyond the schema, meeting the baseline expectation but not surpassing it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a support handoff and specifies three scenarios: human review, escalation, account-specific follow-up. The verb and resource are distinct. However, it does not explicitly differentiate from sibling handoff tools like handoffs.agency or handoffs.partnership, which limits clarity slightly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context when to use (human review, escalation, account-specific follow-up) but lacks explicit guidance on when not to use or how to choose among sibling handoff tools. No exclusion criteria or alternative recommendations are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
handoffs.partnershipRequest Partnership HandoffAInspect
Authenticated — creates a partnerships handoff record for design-partner, ecosystem, training, or advisory conversations needing human review. Persists a PartnershipHandoff row routed to the partnerships inbox; the user is contacted by the team. WHEN TO CALL: user explicitly wants to engage as a design partner, co-marketing/training partner, or evaluate the Blueprint for their org's training programme. ALWAYS confirm with the user before firing — this creates a human-visible partnerships ticket. WHEN NOT TO CALL: for general support / billing / access issues (use handoffs.operator); for paid-engagement enquiries (use handoffs.agency); proactively or as a sales prompt — only when the user has explicitly asked. BEHAVIOR: write-only, single insert, side-effecting (creates a ticket). Auth: Bearer (any plan). UK/EU residency. Response confirms the ticket id + audience so the user can reference it.
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Role or title of the person submitting the partnership inquiry. | |
| topic | No | Partnership topic category. | ecosystem |
| locale | No | Response locale for the handoff acknowledgment. | en |
| reason | Yes | Clear description of the partnership opportunity or inquiry. | |
| website | No | Website of the organization for additional context. | |
| agent_name | No | Name of the agent or client triggering the handoff. | mcp-client |
| organization | No | Name of the organization or company making the partnership inquiry. | |
| trace_summary | No | Optional agent trace summary for operator context. | |
| agent_platform | No | Platform or runtime the agent is running on. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false and idempotentHint=false, which align with a write operation. The description adds an auth requirement ('Auth: Bearer <token>') not present in annotations, providing useful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (one sentence plus auth instruction) with no wasted words. It is front-loaded with the main purpose, though it could benefit from slightly more detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the basic purpose and auth, but given the tool has 9 parameters (1 required) and a presumed output schema, it lacks details on what the handoff entails or the response structure. The schema and annotations carry most of the burden.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add any additional meaning beyond what the schema provides for parameters. The brief auth note is separate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the action (Creates), resource (partnerships handoff), and context (design partner, ecosystem, training, or advisory conversations needing human review). It clearly differentiates from sibling tools like handoffs.agency and handoffs.operator by listing specific topics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'needing human review' provides clear context for when to use this tool. However, it does not explicitly mention when not to use it or compare with sibling tools (handoffs.agency, handoffs.operator), which would be helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
me.add_evidenceAdd Evidence NoteBInspect
Authenticated — append a free-text evidence note to a specific stage in the caller's active course. Notes record concrete implementation observations, decisions, or artefacts that demonstrate progress through a Blueprint principle (e.g. how a delegation boundary was implemented, what approval flow was chosen and why). Persisted as UserStageEvidence rows scoped to (user_id, course_slug, stage_slug). WHEN TO CALL: AFTER the user has articulated something concrete they have built, observed, or decided — not to capture intent or speculation. Pair with me.coaching_context to close evidence gaps. WHEN NOT TO CALL: to log every conversation turn; to record planning, ideas, or todos; on behalf of another user; without the user's awareness (they should know their progress is being recorded). BEHAVIOR: write-only, single insert. Auth: Bearer (Firebase ID token, any plan). UK/EU residency. Notes are visible only to the owning user and are surfaced on me.learning_path / me.coaching_context. Confirms the stage_slug + course_slug pair in the response so the user can see which stage was credited.
| Name | Required | Description | Default |
|---|---|---|---|
| note | Yes | Evidence note to append to the delegation boundary notes for this stage. | |
| stage_id | Yes | ID of the stage to append the evidence note to. | |
| course_slug | Yes | Slug of the course the stage belongs to (e.g. 'agentic-fundamentals'). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate it's a write operation (readOnlyHint=false). The description adds the auth requirement (Bearer token). It does not contradict annotations, but it does not elaborate on side effects or other behavioral traits beyond what annotations convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two brief, front-loaded sentences with no redundant information. It is optimally concise for the given information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the basic operation but lacks context such as constraints, error conditions, or size limits on the note. While an output schema exists, the description does not fully compensate for the missing behavioral context, making it minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and each parameter has a clear description. The main description adds no additional semantic value beyond the schema, so it meets the baseline without exceeding it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (appends a note) and the resource (delegation boundary notes for a course stage), which distinguishes it from sibling tools. However, the term 'delegation boundary notes' may be domain-specific and slightly ambiguous without further context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, nor are there any exclusions or prerequisites mentioned. The description is purely functional.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
me.coaching_contextGet My Coaching ContextARead-onlyIdempotentInspect
Authenticated — returns stages in the caller's active course where recorded evidence is thin relative to the stage's principle requirements. Each thin stage carries the missing principle slugs + a short diagnostic so the caller can suggest the user record concrete evidence. WHEN TO CALL: when the user asks 'what should I work on next' or 'what's weak in my Blueprint progress'; before suggesting which guide/example to consult. Pair with me.add_evidence to close gaps. WHEN NOT TO CALL: to lecture the user on principles they have already satisfied; on every conversation turn (state changes only when evidence is added). BEHAVIOR: read-only, idempotent. Auth: Bearer (any plan). Returns thin_stages list with stage slug, course slug, missing principles, evidence_count, and a coaching_note.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds the auth requirement ('Bearer <token>') which is not in annotations, and clarifies the output nature (stages with thin evidence). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences: first states core functionality, second adds auth context. No redundant words or information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless tool with an output schema, the description is largely complete. It explains the return type and auth. However, it could clarify the definition of 'thin evidence' or link to principles, though not strictly necessary given the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema description coverage is trivially 100%. The description does not need to add parameter info and instead focuses on tool output, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool returns stages where evidence is thin relative to principle requirements. This specific verb-noun combination distinguishes it from siblings like 'me.add_evidence' and 'me.learning_path'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description does not specify when not to use it or mention any preconditions beyond auth.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
me.learning_pathGet My Learning PathARead-onlyIdempotentInspect
Authenticated — returns the caller's Blueprint learning-path state: current course slug, stage progress, certification status (Foundation, Practitioner, Capstone), Capstone track eligibility flags, and the next recommended stage. WHEN TO CALL: the user asks 'where am I', 'what's next', or 'am I Capstone-eligible'; before suggesting next-step coaching content. WHEN NOT TO CALL: as a heartbeat (state changes only when the user completes a stage); to read another user's progress. BEHAVIOR: read-only, idempotent. Auth: Bearer (any plan, including basic). Returns user_email, course_slug, stages list with completion timestamps, certification block, and a next_stage hint.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds authentication requirement ('Auth: Bearer <token>') and lists returned data (course progress, certification status, eligibility). Annotations already indicate read-only and idempotent. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence plus auth note. No superfluous words. Front-loaded with core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given output schema exists, description provides sufficient overview of return data. Auth note covers access. Missing error conditions or rate limits, but not critical for a simple read tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters in schema; schema coverage 100%. Baseline 4 for 0 parameters. Description does not need to add parameter details, but enumeration of returned data is helpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Title 'Get My Learning Path' and description 'Returns your learning path state: course progress, certification status, and Capstone track eligibility' clearly specify the action (return), resource (learning path), and data provided. It distinguishes from sibling tools like 'me.coaching_context' or 'assets.list'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives (e.g., 'me.coaching_context' for coaching, 'guides.get' for guides). No mention of prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
me.validation_historyMy Architect Agent Validation HistoryARead-onlyIdempotentInspect
Pro/Teams — return the authenticated user's architect.validate run history with the Blueprint Readiness Score (0-100), letter grade (A-F), and tier (draft, emerging, production_ready). 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 returned. The run completes server-side and persists; the run_id is surfaced in the first progress notification of every architect.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 calling architect.validate again on the same repository — they tell you which principles regressed since the last run, so you can focus the new review on what is actually changing. Auth: Bearer . Pro or Teams plan required.
| Name | Required | Description | Default |
|---|---|---|---|
| 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
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond the readOnlyHint and idempotentHint annotations, including auth requirements, return format, and regression diff logic. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loaded with the core purpose, and every sentence adds essential information. No superfluous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description fully explains the return data (score, grade, tier) and the regression diff for single-repository queries. It covers both usage modes, auth, and plan requirements, making it complete for agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description enriches them with usage context: for repository, it says to pass the same value as architect.validate; for limit, it mentions capping at 50. This adds value over the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns the authenticated user's architect.validate run history with specific data (score, grade, tier). It distinguishes between behavior with and without a repository argument, and implicitly differentiates from sibling tools like architect.validate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises using this before calling architect.validate again, explaining how it helps focus reviews on regressions. It also mentions the required Pro/Teams plan, providing clear when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
principles.getGet PrincipleARead-onlyIdempotentInspect
Get one Blueprint principle by stable slug. Returns id, title, cluster, definition, rationale, risk-if-violated, implementation heuristics, and linked example slugs (which examples.get can hydrate). Use this when you already have the exact slug from principles.list or principles.search; prefer principles.search when the user describes a topic or failure mode in natural language; prefer principles.list when you need every principle or every principle within a cluster. Returns error_payload on unknown slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Stable slug of the principle (e.g. 'establish-trust-through-inspectability'). |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds that the slug is stable, but does not disclose other behaviors like error handling or permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded, no extra words. Perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity, good annotations, and presence of an output schema, the description is adequate. It covers the core purpose, but could optionally mention what happens if slug is invalid.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already describes the slug parameter with example and note of stability. Description adds no new meaning; schema coverage is 100%, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets a single doctrine principle by its stable slug. It distinguishes from sibling tools like principles.list and principles.search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: when you know the stable slug, use this tool. However, it does not explicitly mention alternatives or when-not to use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
principles.listList PrinciplesARead-onlyIdempotentInspect
List all 10 Blueprint principles with stable slugs, titles, and clusters. Use this when you need the full inventory or want every principle in one cluster (pass cluster slug to filter). Prefer principles.search when the user describes a topic, failure mode, or keyword in natural language. Prefer principles.get when you already know the exact slug and need full detail.
| Name | Required | Description | Default |
|---|---|---|---|
| cluster | No | Cluster slug to filter by (e.g. 'delegation', 'visibility', 'trust', 'orchestration'). Omit to return all principles. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and idempotentHint. The description adds minimal behavioral context (stable slugs), but no further traits like pagination or auth needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence that is front-loaded with the purpose, containing no extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists and the tool is simple (list with one optional filter), the description covers all necessary context, including the notion of stable slugs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description paraphrases the schema's description for the 'cluster' parameter, adding no new meaning beyond what the schema already states.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb 'List' and resource 'doctrine principles', and mentions 'stable slugs' which distinguishes it from siblings like 'principles.get' or 'principles.search' that likely retrieve single or search with complex queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for listing all principles optionally filtered by cluster, but does not explicitly state when to use this vs alternatives like 'principles.get' or 'principles.search'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
principles.searchSearch PrinciplesBRead-onlyIdempotentInspect
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.
| 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
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the description carries minimal burden. It adds that matching occurs against title, definition, rationale, and cluster, but does not disclose other behaviors like ranking, case sensitivity, or result ordering. This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that conveys the purpose and matching fields with no unnecessary words. It is front-loaded and earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple search nature, the description adequately covers what the tool does. The output schema exists separately, and annotations cover safety. Minor gaps: it could mention that it is read-only (already in annotations) or describe return structure, but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description repeats the schema's info about matching fields without adding new semantics about parameter formatting or constraints. No extra value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it searches doctrine principles by text query and specifies the matching fields. It distinguishes the tool's purpose from siblings by focusing on 'principles', but does not explicitly differentiate from other search tools like examples.search or guides.search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., examples.search, guides.search). The description lacks 'use this when' or 'instead of' statements, leaving the agent to infer appropriate usage from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
signals.feedbackSubmit FeedbackAInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate mutation (readOnlyHint: false). Description adds that contact_email is only stored when permission_to_follow_up is true, and this is confirmed in the response. Also emphasizes never proactive, adding behavioral safety context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences convey purpose, a behavioral caveat, and usage guideline. No wasted words, highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description covers when to use, data handling, and caller anonymity. With 9 optional parameters fully described in schema and an output schema present, the description provides adequate context. Could mention outcome confirmation but not necessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%. Description adds minimal extra meaning beyond confirming the conditional storage of contact_email and mentioning a response confirmation. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States 'Records explicit user feedback' clearly. Distinguishes usage with 'only when user explicitly requests; never proactively', but does not explicitly differentiate from sibling tools like signals.report.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to call ('only when user explicitly requests; never proactively') and notes anonymous callers are welcome. No mention of alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
signals.reportReport Value EventAInspect
Pro/Teams — records a value moment (review_confidence, runtime_risk_found, regression_caught, recommendation_taken) after a successful architect.validate or design session. Each event captures event_type, surface_used (mcp/web/cli), perceived_value (1-5), and an optional brief_context — structured fields only, NO prompts or code stored. WHEN TO CALL: after architect.validate returns a clearly useful result AND the user has acknowledged the value (or you ask them "would you rate this 1-5?"). Validate's response carries an explicit next_step instruction telling the agent to OFFER this call — surface that offer to the user. WHEN NOT TO CALL: silently or without the user's awareness; on every validate (only after a clear value moment); to capture intent or speculative value. If the user declines, do not retry within the same session. BEHAVIOR: write-only, single insert into ValueEvent. Auth: Bearer , Pro or Teams plan required. UK/EU residency. Do NOT include proprietary code, prompt content, or PII in brief_context — it surfaces in admin AI-visibility dashboards. Expect a 1-line acknowledgment in the response; the structured feedback is then aggregated server-side.
| Name | Required | Description | Default |
|---|---|---|---|
| team_size | No | If the user mentions their team size during the session, record it here. Do not ask for it explicitly — only capture if volunteered. | |
| event_type | Yes | Pick the type that best matches what just happened: 'review_confidence' — architect.validate returned aligned; 'runtime_risk_found' — architect.validate found violations; 'workflow_clarity' — principles/examples clarified a design decision; 'agent_setup_success' — user successfully wired up an agent or MCP tool; 'onboarding_helped' — user understood how to start using the Blueprint; 'research_time_saved' — user found relevant doctrine faster than expected; 'team_alignment' — Blueprint helped align a team on agentic design; 'other' — use only if none of the above fit. | |
| surface_used | No | Where the value was experienced. Use 'mcp' when called from Claude Code, Cursor, Windsurf, or any MCP client. Use 'principles' if the user was browsing or searching principles. Use 'examples' if the user was reading implementation examples. Use 'for-agents' if the user came via the /for-agents page. Use 'learn' or 'certification' for course-related sessions. | |
| brief_context | No | 1–2 plain-English sentences summarising what was helpful. Example: 'Validation identified a missing approval gate before email send.' No code snippets, no proprietary content, no user PII. Max 500 chars. | |
| workflow_stage | No | Infer from what the user was doing: 'exploring' — reading doctrine, browsing principles; 'designing' — planning architecture or agent flows; 'implementing' — writing or refactoring code; 'reviewing' — running architect.validate on existing code; 'shipping' — preparing for production or deployment. | |
| perceived_value | No | Ask the user: 'On a scale of 1–5, how valuable was this session?' Map their answer directly: 1=low, 5=high. Do not guess — only set this if the user gave an explicit score. | |
| would_recommend | No | Ask the user: 'Would you recommend the Blueprint to a colleague?' Set true/false based on their answer. Only set if asked — do not assume. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate a write operation (readOnlyHint=false). Description adds detail: only structured fields stored, no prompts/code, and a constraint on brief_context. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, each sentence adds value. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers usage, data storage constraints, and anonymity. Output schema exists so return values don't need explanation. Could benefit from mentioning when not to use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all parameters. The description adds no extra meaning beyond the schema fields, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Records a value moment') and resource, but does not explicitly differentiate from sibling 'signals.feedback', which could be similar.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: offer once per session after a clear success signal, never silently. Includes that anonymous callers are welcome, but no exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
team.summarizeSummarize Team UsageARead-onlyIdempotentInspect
Pro/Teams — summarises the caller's tool-usage patterns and value signals over a configurable window (default 30 days). Returns tool_call_counts, top principles cited in validate runs, value_event_counts by event_type, and an aggregate readiness trend. WHEN TO CALL: the user asks 'how is the Blueprint helping me/my team', 'what should I explore next', or 'show me my Blueprint usage'. WHEN NOT TO CALL: proactively or on every conversation turn (the summary is an explicit retrospective, not telemetry); to compare users (returns only the caller's own data). BEHAVIOR: read-only, idempotent over the same window. Aggregates from AIToolCallLog + ValueEvent + AIValidationRunLog. Pass private_session=true to bypass server-side logging for this summary call (the underlying historical data still exists; only this read is untracked). Auth: Bearer , Pro or Teams plan. UK/EU residency.
| Name | Required | Description | Default |
|---|---|---|---|
| days_back | No | Number of days of usage history to include in the summary. | |
| private_session | No | Set to true to skip logging this summary call. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds value by explaining it uses log data, mentions auth requirement ('Auth: Bearer <token>'), and discloses that setting private_session skips logging, providing behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. First sentence defines purpose and data source. Second sentence gives usage guideline and a parameter hint. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has output schema (not shown but in context), so return value description is unnecessary. The description covers purpose, usage context, authentication, and a special parameter. Complete for the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% coverage with descriptions for both parameters. The description adds only 'private_session=True skips logging', which is redundant with schema. Baseline 3 is appropriate as schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool summarizes tool usage patterns and value signals from log data, with specific verb 'summarises' and resource 'team usage'. It distinguishes from siblings like 'guides.get' or 'assets.list' by focusing on usage patterns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Offer when user asks how the Blueprint is helping or what to explore next; not proactively', giving clear when-to-use and when-not-to-use guidance. No explicit alternative is named, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!