AI Design Blueprint
Server Details
Enterprise-safe agentic AI design doctrine. Read-only MCP, UK/EU residency, zero-training policy.
- 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 4.1/5 across 21 of 21 tools scored. Lowest: 3.3/5.
Every tool targets a distinct function: validation, asset listing, cluster/principle/example/guide access, various handoff types, user progress tracking, feedback, and team summarization. No two tools are ambiguous.
Tools follow a domain.action pattern (e.g., architect.validate, clusters.list). Most are verb_noun, but a few like handoffs.agency use a noun, and me.coaching_context uses a noun phrase. Overall consistent but with minor deviations.
21 tools is on the higher side but justified by the broad scope covering principles, examples, guides, handoffs, user progress, and feedback. Each tool serves a clear purpose, though some consolidation could reduce count.
The tool surface covers the main domain well: CRUD-like operations for principles/examples/guides, multiple handoff types, user evidence tracking, and feedback. Minor gaps exist (e.g., no update/delete for evidence or handoffs) but these are acceptable for the intended use.
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?
Discloses key traits: bounded synchronous one-shot (20-40s), atomicity, no in_progress/cancel/resume, failure recovery via fresh cert, retry budget, enterprise-safe data handling (transient, no training, JSON-escaped), UK/EU residency, auth method. Adds context beyond annotations (readOnlyHint=false, openWorldHint=true). 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?
Description is dense but well-structured, front-loaded with core purpose and behavior. Some redundancy (e.g., repeated safety details) but overall efficiently conveys all necessary information given complexity.
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?
Comprehensive coverage: purpose, usage guidelines, behavior, parameter semantics, failure recovery, data handling, privacy, auth, eligibility gate, and effect on persistence (updates result_json). No output schema but return behavior implied. Fully complete given tool 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?
Schema coverage is 100% (baseline 3). Description adds meaning: for 'code', explains why it must be verbatim, ties to enterprise-safety; for 'run_id', specifies ownership and retrieval from validate response. This extra context justifies a 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 clearly identifies the tool as 'second-pass adversarial certification of an architect.validate run' and specifies it 'Mints the certified production_ready badge' or caps to 'C/emerging'. This distinctively differentiates it 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?
Explicitly states when to use (after architect.validate returns production_ready A/B) and when not (re-validate instead of stateful cert), along with eligibility gate conditions (caller owns run, tier production_ready, <24h old, not certified, retry budget). Provides alternative recovery path on failure.
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?
Beyond annotations (readOnlyHint=false, openWorldHint=true), the description details transient processing, privacy controls (private_session), data residency, authentication, and that it calls an external LLM. 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?
Front-loaded with purpose, then returns, then privacy details. Each sentence adds value, but the privacy section is somewhat long. Still well-organized 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?
The description explains what the tool returns (coverage, findings, recommendations) but lacks detail on output structure. Given no output schema, more specifics would help. Privacy details are good, but 'Blueprint doctrine' is not defined or linked to sibling resources.
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% description coverage, so parameters are already well-documented in schema. The description does not add significant meaning beyond what the schema provides, warranting baseline 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 starts with a clear verb and resource: 'evaluate code, a workflow, or an architecture description against the Blueprint doctrine.' It distinguishes itself from sibling tools that provide reference data (e.g., principles.list) by focusing on evaluation and recommendations.
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 states what to evaluate but does not explicitly mention when to avoid this tool or suggest alternatives among siblings. The context is adequate for basic usage but lacks exclusion criteria.
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 important behavioral traits beyond annotations: N parallel LLM calls, wallclock ~80-120s, cost N×LLM bill, private_session=True for children, consolidated row persisted but children not, recovery pattern, typed failures including consensus-specific quorum failure. No contradiction with annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false).
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 well-structured with clear sections (overview, when to call, when not to call, behavior, auth, inputs, output, recovery, typed failures). It is front-loaded with core purpose. Every sentence serves a purpose; no fluff.
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 having no output schema, the description explains the output in detail (score_consensus_median, score_stdev, etc.). It covers recovery, typed failures, authentication, and input shape. For a complex tool with 9 parameters, it is highly complete.
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 meaningful extra context for the 'n' parameter (default 3, variance signal visible at N=3, capped server-side) and reiterates the importance of sending full implementation_context verbatim. This adds 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 the tool's purpose: 'N-shot CONSENSUS doctrine review of agentic code.' It explains it runs parallel architect.validate calls with private_session=True and aggregates results. It distinguishes from the sibling tool architect.validate by specifying 'consensus mode' vs single-shot, making the purpose unambiguous.
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 provides 'WHEN TO CALL' (wants an honest first-pass score) and 'WHEN NOT TO CALL' (needs iteration delta against prior run, for which architect.validate should be used). This gives clear context for tool 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 readOnlyHint and idempotentHint as true, so the description does not need to restate safety. It adds no additional behavioral insight 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?
A single, clear sentence with no superfluous words. It conveys the essential information efficiently.
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 list tool with no parameters, the description adequately conveys the result type. However, it does not mention potential omissions like pagination or sorting, though these may not be applicable.
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, and the input schema coverage is 100%. The description adds no parameter details, but with no parameters, a baseline of 4 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 specifies the action ('List') and the resource ('downloadable doctrine and agent asset artifacts'), which distinguishes it from other list tools like clusters.list or guides.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?
The description implies use for listing specific asset types but does not explicitly state when to use it versus alternatives. However, the resource name ('assets') and context from siblings make the usage context reasonably clear.
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=true and idempotentHint=true, which handle safety. The description adds no additional behavioral context beyond the core action, but does not contradict 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, efficient sentence with no extraneous information. Every word contributes to the 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?
Given the simplicity of the tool (one parameter, no output schema, clear annotations), the description is largely complete. It could benefit from briefly noting what a principle cluster is, but it is sufficient for an agent with domain knowledge.
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 a well-described 'slug' parameter. The description's 'stable slug' is redundant with the schema. No new meaning is added 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 action 'Get' and the resource 'principle cluster' by its 'stable slug', distinguishing it from the sibling clusters.list which returns a 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 vs alternative tools (e.g., clusters.list). The description implies it's for fetching a single cluster, but lacks exclusionary or comparative context.
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. Description adds that it returns 'stable slugs and linked principle titles', providing useful 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?
Single sentence, no wasted words, front-loaded with the action and key 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 simple listing tool with no parameters and no output schema, the description is adequate. Could mention if there is pagination or ordering, but likely not needed.
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 baseline is 4. Description does not need to add parameter info, and it does not.
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?
Clearly states the tool lists principle clusters and specifies the returned fields (stable slugs and linked principle titles). This distinguishes it from siblings like 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?
No explicit guidance on when to use over alternatives like clusters.get or principles.list. Usage is implied but not elaborated.
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 provide readOnlyHint and idempotentHint. Description adds only 'curated' qualifier, no new 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?
Single sentence, perfectly front-loaded, no redundant words. Every word 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?
For a simple get-by-slug tool with good annotations and full schema, description is adequate. Missing output schema but not critical for this action.
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 clear example for 'slug'. Description adds no additional meaning; baseline 3 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 'Get a curated example by its stable slug', specifying verb (Get), resource (curated example), and unique identifier (slug). Distinguishes from sibling 'examples.search' which would be for searching.
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?
Implied usage: when you have a slug. No explicit when-not or alternatives mentioned. Context is clear but lacks depth.
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 declare readOnlyHint and idempotentHint, which are consistent with the 'Search' operation. The description does not contradict annotations and adds no misleading behavior. It could mention sorting or result ranking, but the annotations already assure safety, so the bar is lower. Score 4 for consistency and sufficient transparency given the tool's simplicity.
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 front-loaded with the essential action and resource, followed by filter dimensions. Every word earns its place; no redundancy or vagueness.
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 5 parameters, 1 required, no output schema, and sibling tools, the description covers the search dimensions but omits return behavior (e.g., pagination, ranking, default sort). For a search tool, this is a notable gap. Without output schema, completeness is lower than ideal.
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 input schema fully documents each parameter. The description only lists the filter dimensions without adding new meaning (e.g., syntax, format, or behavior). Per guidelines, baseline is 3, and no extra value is provided.
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 precise language: 'Search curated examples by text, principle coverage, difficulty, and library.' It clearly identifies the action (search) and resource (curated examples), and distinguishes from sibling tools like examples.get (presumably fetch by ID) and guides.search (different resource).
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 searching examples but does not explicitly state when to use this tool versus alternatives (e.g., examples.get, guides.search) or provide exclusions. The context is clear but lacks explicit guidance, leaving the agent to infer from tool names.
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 declare readOnlyHint=true and idempotentHint=true; description adds no behavioral context beyond repeating the purpose.
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 with action and resource, 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?
Adequate for a simple get operation; lacks return format details but no output schema expected.
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 slug fully (100%); description adds example slugs but no new meaning.
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?
Clearly states it retrieves a full application guide by stable slug, differentiating from siblings like guides.list and 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?
Implies use when slug is known, but no explicit guidance on when to use list/search instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
guides.listList Application GuidesBRead-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 indicate readOnlyHint and idempotentHint, so the description does not need to restate safety. The description adds minor context about the content of the guides but does not disclose output format or pagination behavior.
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 front-loaded with the verb 'List' and provides essential information without any wasted 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?
Without an output schema, the description should specify what the returned data contains (e.g., guide titles, IDs). It only mentions the thematic focus but omits details about the structure of the list, leaving the agent uncertain about how to use the result.
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 no parameters, so the description cannot add parameter semantics beyond what the schema provides. The baseline score for zero parameters is 4, and the description does not detract from that.
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 lists application guides and specifies the thematic content (blueprint principles applied to engineering challenges). While it distinguishes from siblings like 'guides.get' and 'guides.search' by being a list operation, it could be more explicit about what 'application guides' are.
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 'guides.search' or 'guides.get'. The description lacks explicit when-to-use or when-not-to-use instructions.
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 provide readOnlyHint and idempotentHint, so the tool's safety profile is clear. The description adds value by explaining the scope of the search (all guide content including sections and action items), which is not inferable from annotations alone.
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 with no extraneous words, front-loading the action and scope. It efficiently conveys 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?
While the description explains what the search covers, it omits information about the return format (e.g., guide IDs, scores, snippets). Since there is no output schema, the description could be more complete by hinting at the response structure.
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%; the schema already documents both parameters. The description restates that 'query' is a free-text search matching all content, but does not add significant new semantic information 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 specifies a verb ('Search'), a resource ('application guides'), and the scope ('by text query'). It also details that matching covers all content including section answers and action items, which clearly distinguishes it from sibling tools like guides.get or guides.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?
The description states the tool's function but provides no guidance on when to use it versus alternatives such as guides.get (for a specific guide) or other search tools (examples.search, principles.search). No when-not-to-use instructions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
handoffs.agencyRequest Agency HandoffAInspect
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 readOnlyHint=false, idempotentHint=false, and openWorldHint=true. The description adds that a Firebase Bearer token is required, which is extra security context beyond the annotations. However, it doesn't describe side effects, persistence, or what happens after submission (e.g., confirmation, call scheduling).
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 long, front-loaded with the core purpose. Each sentence adds necessary information: purpose, scope details, usage guidance, and authentication requirement. No redundant or filler content.
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 10 parameters and no output schema, the description should clarify the expected outcome (e.g., acknowledgment, call initiation). It mentions 'discovery call' but does not explain what the agent will receive after submission. This missing information reduces completeness.
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 10 parameters with 100% description coverage. The description adds value by explaining the meaning of the four engagement scopes in detail (workflow sprint, proof-of-concept, pilot support, advisory), which helps the agent choose appropriate values for 'reason' and 'support_type'. This goes beyond the schema 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 a founder-led discovery call. It lists four specific scopes (workflow sprint, proof-of-concept, pilot support, advisory), and the name 'handoffs.agency' differentiates it from siblings like '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?
The description says 'Use this when the user has identified a need for hands-on expert support beyond self-service learning.' This gives a clear condition for use. It doesn't explicitly specify when not to use or mention alternatives, but the context implies that other handoff tools handle different scenarios.
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 the tool is mutating (readOnlyHint=false) and not idempotent. The description adds an authentication requirement ('Auth: Bearer <token>') but does not disclose other behavioral details such as rate limits or side effects like ticket creation.
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 plus an auth note, making it concise and front-loaded. While it could be expanded slightly, it avoids unnecessary text and effectively communicates the core 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?
With 7 parameters fully documented in the schema and no output schema, the description provides minimal additional context. It lacks explanation of return values, differentiation from sibling tools, and post-call effects, making it adequate but incomplete.
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 100% parameter description coverage, so the schema already explains each parameter. The description does not add extra meaning beyond the schema, resulting in a baseline score.
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 for human review, escalation, or account-specific follow-up, using a specific verb and resource. It implicitly distinguishes from sibling tools (handoffs.agency and handoffs.partnership) by specifying the operator 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?
The description provides clear context on when to use the tool: when an agent needs human review, escalation, or account-specific follow-up. However, it does not explicitly exclude other scenarios or mention alternatives like sibling tools.
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 already indicate readOnlyHint=false and idempotentHint=false. The description adds the auth requirement (Bearer token) which is helpful, but does not elaborate on side effects or other behavioral nuances 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 two sentences with no redundant information. It front-loads the primary action and includes essential auth info. Every sentence contributes 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?
For a 9-parameter tool with no output schema, the description provides a basic overview and auth info but lacks details about return behavior, error conditions, or operational context (e.g., what happens after handoff). The schema fills some gaps, but completeness is moderate.
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% and each parameter has a description. The tool description summarizes the purpose but adds no extra meaning beyond what the schema already provides, so baseline score of 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 tool creates a partnerships handoff for specific conversation types (design partner, ecosystem, training, advisory) needing human review. It uses a specific verb and resource, and the context is distinct from siblings like handoffs.agency.
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 partnership inquiries needing human review but does not explicitly contrast with sibling tools or specify when not to use it. No exclusions or alternatives are provided.
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 NoteAInspect
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 non-readOnly and non-idempotent. The description adds that it requires a Firebase Bearer token, which is useful authentication context. It also implies mutation by saying 'append.' However, it doesn't describe side effects (e.g., whether notes are appended each time) or return behavior, which would enhance transparency.
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, well-structured paragraph with no redundant information. It front-loads the core action, provides context, includes an example, and ends with a requirement. 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 tool's simplicity (3 required params, no output schema), the description covers the main action, usage context, and authentication. It lacks details on return values or error conditions, but for an append operation, it is mostly complete. The absence of output schema is not a major gap here.
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 clear parameter descriptions in the schema (e.g., 'Evidence note to append for this stage'). The description adds an example of content (e.g., 'how a delegation boundary was implemented') but does not substantially extend the meaning beyond what the schema provides. 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 uses specific verbs ('append', 'record') and identifies the resource ('evidence note to a specific stage'). It distinguishes between capturing intent and recording observations, which clarifies purpose and differentiates from potential sibling tools that might deal with intent.
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 states when to call: 'after the user has articulated something they have built or observed, not to capture intent.' This provides clear guidance. However, it does not explicitly name alternative tools or scenarios when not to use it.
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 authentication details (Bearer <token>) and clarifies the output type (stages with thin evidence). No contradiction 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 extremely concise, with two sentences containing no fluff. Every sentence adds value: one defines the purpose, the second specifies authentication.
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 no parameters and good annotations, the description covers the core functionality and auth requirement. However, it does not explain what 'stages' or 'principle requirements' refer to, which could be inferred from sibling tool names but is a minor gap.
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?
With zero parameters and 100% schema coverage, the description adds no parameter information, which is acceptable. The baseline for 0 params is 4, as the schema fully covers parameter semantics.
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 returns stages where evidence is thin relative to principle requirements. This is a specific verb-resource combination that distinguishes it from siblings like me.add_evidence or 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?
The description implies usage for identifying weak evidence stages, but does not explicitly specify when to use it versus alternatives or provide any exclusion criteria. No guidance on prerequisites or context.
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?
Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds that it returns state and requires authentication, which is useful but not extensive. 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 extremely concise: two sentences, zero wasted words. The most important information (purpose) is front-loaded, followed by auth requirement. Every sentence 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 no parameters and no output schema, the description provides a reasonable outline of what is returned (course progress, certification status, Capstone track eligibility). It could be more detailed about the structure, but it is sufficient for a simple 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?
The input schema has no parameters, so schema description coverage is 100%. The description does not add parameter details because none exist. Baseline for zero parameters is 4, and the description does not need to compensate.
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 'returns' and the resource 'learning path state' and lists specific components (course progress, certification status, Capstone track eligibility), making the purpose unambiguous. It distinguishes this tool from siblings by focusing on the user's personal learning path, which is unique among the listed tools.
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 includes authentication requirements ('Auth: Bearer <token>'), providing clear context for use. While it does not explicitly mention when not to use or alternatives, the tool is singular in its domain (no sibling tools for learning paths), so the context is sufficient.
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?
Beyond annotations (readOnlyHint, idempotentHint), description adds plan requirements (Pro/Teams), auth method (Bearer token), behavior with and without repository argument, and return details including regression diff. 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?
Description is multi-sentence but each sentence adds value (plan, auth, behavior, usage advice). Could be slightly more concise, but well-structured and front-loaded with critical info.
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?
No output schema, but description fully explains returned fields (score, grade, tier, regression diff). Covers two modes of operation, auth, plan requirement, and best practice usage. Highly complete for a tool of this 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 and good descriptions. Description adds context: repository should match architect.validate argument, limit capped at 50, and explains the effect of omitting repository. Enhances semantic understanding beyond 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?
Description states specific verb ('return'), resource ('architect.validate run history'), and adds scope ('with repository or without') that distinguishes it from sibling architect.validate. Clearly describes what the tool does and how it differs.
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 advises using this before architect.validate to avoid re-flagging fixed issues. Provides context on when to call (before a re-run), but does not explicitly mention when not to use it, though that is implied by the sibling tool's purpose.
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=true and idempotentHint=true, so the tool is safe and repeatable. The description adds that the slug is 'stable', meaning the identifier does not change, which is valuable behavioral 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 sentence that clearly conveys the action, object, and identifier. No wasted words, and the most critical information is front-loaded.
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 single-retrieval tool with one required parameter and comprehensive annotations, the description is fully adequate. It doesn't need to describe return values given the tool's straightforward nature.
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 covers the slug parameter with a description (100% coverage). The description adds that the slug is 'stable' and provides an example, which adds slight additional meaning to the parameter's semantics.
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 uses specific verb 'Get' and resource 'doctrine principle', with the identifier 'stable slug'. This clearly distinguishes it from sibling tools like 'principles.list' (multiple) and 'principles.search' (filtered), leaving no ambiguity.
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 specific slug for a principle. It doesn't explicitly state when not to use it or name alternatives, but the context of 'single' retrieval 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?
Adds context beyond readOnlyHint and idempotentHint by noting 'stable slugs' and implying no side effects; 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?
Single, front-loaded sentence with no wasted words; efficiently conveys purpose and key option.
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?
Complete for a simple list tool with one optional param; no missing info given annotations and 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?
Schema coverage is 100%; description adds example and clarifies behavior of omitting the parameter, exceeding schema details.
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 lists doctrine principles with stable slugs and optional filtering, distinguishing it from get and search siblings.
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?
Implicit guidance via filtering option and sibling context; lacks explicit when-not-to-use but is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
principles.searchSearch PrinciplesARead-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=true and idempotentHint=true, so the agent knows it's safe and repeatable. The description adds behavioral context: results are 'closest matches ranked by relevance' and lists return fields (title, cluster, definition, rationale, implementation heuristics). This goes beyond what annotations provide, earning a high score.
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 two sentences: the first states the core function, the second gives usage guidance and preference over a sibling. Every sentence is necessary and impactful, with the main action front-loaded. No wasted 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 having no output schema, the description explicitly lists all return fields (title, cluster, definition, rationale, implementation heuristics) and mentions ranking. For a search tool with only two parameters, this provides complete context for an agent to understand what it will receive.
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 the schema already documents both parameters. The description adds value for the query parameter by specifying it is matched against 'principle title, definition, rationale, and cluster,' which clarifies search scope. For limit, no extra info is added, but the description overall provides meaningful context that supplements 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 starts with 'Search Blueprint principles by free-text query and return the closest matches ranked by relevance,' which is a specific verb+resource. It clearly distinguishes from sibling tools like principles.list (all principles) and principles.get (specific principle) by mentioning the use case of finding principles related to a design challenge or keyword.
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 'Prefer this over principles.list when you have a specific topic in mind rather than wanting all principles,' providing direct guidance on when to use this tool versus an alternative. This is exceptional for helping an agent choose correctly.
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 readOnlyHint=false and idempotentHint=false. The description states 'Records explicit user feedback', confirming mutation. It also adds behavioral detail that 'contact_email stored only when permission_to_follow_up is true — confirmed in response.' This goes beyond annotations. 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?
The description is extremely concise with two sentences, front-loaded with purpose and usage guidance. Every word serves a function, and there is no redundancy or 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?
Given 9 optional parameters and no output schema, the description covers the main behavior and usage constraint adequately. It could mention return values or confirmation, but the context is sufficient for an experienced agent. Minor gap in not describing what the response contains beyond 'confirmed'.
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 baseline is 3. The description does not add additional meaning beyond what is in the schema's parameter descriptions. The only extra is the condition linking contact_email to permission_to_follow_up, but that is also noted in the parameter description. Thus, no significant value added over 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 tool records explicit user feedback, specifies it is open to all callers with no auth required, and emphasizes the verb 'Records' and resource 'user feedback'. It also distinguishes itself from sibling tools by focusing on feedback submission and not other signals.
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 explicit when to use: 'Call ONLY when the user explicitly says they want to give feedback; never proactively.' This is strong guidance. However, it does not name alternative tools (e.g., signals.report) for when not to use, but the directive is clear enough.
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?
Beyond annotations, the description reveals that it is a write operation (mutates state), requires a Pro/Teams Bearer token, and logs structured fields only (no prompts/code). It also specifies that the tool should be offered only after explicit success signals, not silently.
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 at four sentences, front-loading the purpose and key constraints. It avoids unnecessary detail, though a more structured format could improve readability slightly.
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 tool with 7 parameters and no output schema, the description covers essential aspects: when to use, auth, data privacy, and behavior. It omits error handling or success feedback, but these are not critical for basic usage.
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?
With 100% schema description coverage, the schema already thoroughly documents each parameter. The description adds little additional parameter-level meaning beyond the schema, meeting the baseline expectation but not 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 tool records a value moment after a successful session. It specifies the exact resource ('value moment') and action ('records'), and distinguishes it from other tools like signals.feedback by focusing on success signals and structured fields.
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 guidance on when to use the tool ('after a clear success signal' and 'offer once per session'), what not to include ('no proprietary content'), and authentication requirements. However, it does not explicitly mention alternatives or when not to use it.
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 provide readOnlyHint and idempotentHint. Description adds crucial context: enterprise-safe with private_session flag to bypass logging, UK/EU data residency (Cloud Run europe-west2), and Bearer auth. 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 with clear front-loading of purpose, then usage, then security details. Every sentence adds value without redundancy.
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 purpose, usage, security, and residency. However, no output schema and description does not specify what the summary contains (e.g., format or metrics), leaving slight ambiguity for the 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?
Schema coverage is 100% with both parameters having descriptions. Description adds context about private_session bypassing server-side logging, but this is largely consistent with the schema description. Baseline 3 applies since schema already covers semantics adequately.
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 'summarises your tool usage patterns and value signals from log data', which is a specific verb+resource. It distinguishes from sibling tools (e.g., assets.list, clusters.list) that do not perform summarization.
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?
Explicit guidance: 'Offer when user asks how the Blueprint is helping or what to explore next; not proactively.' This tells the agent when to use it, though it lacks explicit mention of alternatives among siblings.
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!