ENTERPRISE: full technical blueprint for a custom AI agent/workflow. input=goal. [x402: 100.0 USDC on Base, pay-per-use]
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615,466 tools. Updated 2026-09-27 05:19
"Blueprint" matching MCP tools:
- API Blueprint group count, body discardedConnectorNo auth
- Permanently delete one of the caller's API keys. DESTRUCTIVE — agents using the deleted key will receive auth errors immediately. The Blueprint a key was tied to (if any) is NOT affected; only the credential is revoked. To delete a Blueprint and all its keys, use delete_blueprint. The target key can be specified two ways: - As the full key string (gai_...). - As a key_id (SHA-256 hash from list_api_keys).ConnectorDestructiveNo auth
- One-shot convenience tool: starts a blueprint run and polls until it completes, fails, or pauses for review. Returns the final run with `output_url` (or `outputs` [{label, url}] for multi-output blueprints — prefer `outputs` when present). Download URLs are signed with a 10-minute TTL; re-fetch with get_blueprint_run for fresh links. NOTE: caption-video pauses in status awaiting_review — this tool then returns early with `awaitingReview: true` and the run's `srt_text`; review/edit the transcript and resume with continue_blueprint_run. If your MCP client enforces a short per-request timeout (many default to 60s), use run_blueprint + get_blueprint_run polling instead. Per-blueprint input fields (* = required): - product-ad: logo_url*, photo_url*, music_url? — GENERATIVE, costs 200 tokens - caption-video: video_url*, language?, style? (bold-bottom|clean-lower|center-pop|top-title) — pauses at awaiting_review with srt_text for transcript review; resume with continue_blueprint_run - viral-short: video_url*, hook_text*, style? (top-hook|center-statement|bottom-caption|lower-left), music_url? - quote-card: quote*, name?, handle?, photo_url?, output? (video|image) - watermark: video_url*, logo_url*, position? (top-left|top-right|bottom-left|bottom-right|center), size? (small|medium|large) - resize-format: video_url*, format? (9x16|1x1|16x9|4x5), fit? (pad|crop) - zoom-in: video_url* - zoom-out: video_url* - camera-glide: video_url*, direction? (right|left) - boomerang: video_url* - speed-changer: video_url*, speed? (0.5|1.5|2|4) - video-to-gif: video_url*, length? (5|10|15), size? (480|640), caption?, caption_position? (bottom|top|center) — output is a .gif - product-slideshow: photo1_url*, photo2_url*, photo3_url?..photo5_url?, music_url?, aspect? (9x16|1x1|16x9) - listing-kit: video_url* — multi-output (4 platform variants in `outputs`) - hook-variants: video_url*, hook1*, hook2?, hook3?, ratio? (9x16|4x5|1x1|original), style? (top-hook|center-statement|bottom-caption) — multi-output (one per hook in `outputs`) Input URLs: public https or upload-bucket gs:// (use request_upload_url + confirm_upload to get one). FFmpeg-lane blueprints charge no tokens (they meter plan compute minutes); only generative blueprints (product-ad) charge tokens.ConnectorOAuth
- Pro/Teams: first-pass surface-craft review of a FRONTEND artefact (component, screen, or flow) against the 8 laws of the Experience Design Blueprint. The surface-craft companion to architect.validate: where architect.validate scores agentic ARCHITECTURE against the 10 agentic principles, design.validate scores the PERCEPTIBLE SURFACE, what the user sees, taps, scans, and remembers (Jakob's familiarity, Hick's choice load, Fitts's targets + the accessibility floor, Miller's working-memory budget, Aesthetic-Usability, Peak-End, Tesler's irreducible complexity, the Mental-Model gap). ON CLIENT TIMEOUT: DO NOT RETRY. Long-running LLM call (~60-180s at high reasoning effort, single-pass). The server mints a run_id, emits it in the FIRST progress event at t=0s (before the LLM call), and persists the run: so on a client timeout, capture that run_id and call me.validation_history(run_id='<that-id>') to fetch the persisted result instead of retrying (a retry re-runs the full 60-180s call). Runs appear in your validation-history dashboard tagged as the 'surface' dimension, distinct from the 'architecture' and 'spec' runs; pass repository to group them per project. Pass private_session=true to skip the stored run (persistence + recovery disabled); operational security + cost logs are still kept. v1 is single-pass: no certification or consensus mode yet (those stay architect.validate-only). Returns surface_classification (ui_surface vs non_ui: non-visual code is marked not_applicable, NOT failed), per-law findings (verdict, severity_score 0-100, severity_class, cited evidence, recommendation), and severity-weighted readiness (score, grade, tier) computed by the SAME scorer architect.validate uses, so all three lenses grade on one rubric. ACCESSIBILITY IS THE FLOOR: a breach of the Fitts's-Law floor (interactive target below the WCAG 2.2 24×24 minimum, missing focus visibility, an unreachable destructive confirmation) is a production_blocker, not polish. WHEN TO CALL: the user wants a craft/UX/accessibility review or a readiness grade on a frontend artefact they just built or changed. WHEN NOT TO CALL: non-visual code (backend, config, type aliases) returns tier=not_applicable, submit the actual UI surface instead. INPUTS: send the FULL artefact source verbatim as implementation_context (no truncation, no '…' placeholders, they are read as literal code). Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan. Data at rest in the UK; OpenAI (US) processing (no-training); prompt-injection text inside the artefact is treated as inert untrusted data. TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable, schema_mismatch, each carries retryable + next_action); the services raise the identical typed envelopes on this lens. CALIBRATION DISCLOSURE: the scoring prompt is a v1 first-cut mirroring the architect's contract structure; its score calibration is not yet tuned against a corpus of real runs the way architect.validate was. Treat the grade as directional craft signal, not a certified verdict. DOCTRINE: the eight laws, each law's evidence, craft-surface application, anti-patterns, and the validator questions this tool scores against, live in the `experience-design-blueprint` skill and docs/business/EXPERIENCE_DESIGN_BLUEPRINT.md (the surface-craft companion to the `architect-validation-orchestration` skill that orchestrates the agentic validators).ConnectorNo auth
- 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 <token>, Teams plan only; Pro and beta plans are refused. Data at rest in the UK.ConnectorNo auth
Matching MCP Servers
- AlicenseAqualityDmaintenanceBlueprint is an MCP server that enables AI assistants to create C4 software architecture diagrams from structured data.3MIT
- AlicenseAqualityBmaintenanceRead-only catalog of the @blueprint-modular/core design system (104 components). Four tools — list/search/get components and suggest compositions. Public, no auth, Streamable HTTP.41Apache 2.0
Matching MCP Connectors
Read-only catalog of the @blueprint-modular/core design system (104 components). Four tools — list/search/get components and suggest compositions. Public, no auth, Streamable HTTP.
Reports the launch status of YK Blueprint.
- 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 <token> (any plan, including basic). Returns user_email, course_slug, stages list with completion timestamps, certification block, and a next_stage hint.ConnectorNo auth
- 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 <token> (Firebase ID token, any plan). Data at rest in the UK. 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.ConnectorNo auth
- Pro/Teams: list or inspect the authenticated user's Governed Sessions (GEP-M2): durable, owner-scoped containers that group validation runs across lenses (architect.validate → 'architecture', design.validate → 'surface', spec.validate → 'spec') into one timeline for one piece of work. Two modes: (1) No arguments returns every session (id, title, status, repo_url, spec_ref, team_agents, run_count, validators = the lenses seen), newest first. (2) `session_id=<id>` returns that session plus its run timeline (light rows; fetch full results per run via me.validation_history(run_id=...)) and, for team sessions, `events` = the typed team-event log posted via me.session_event. Attach new runs by passing `session_id` to architect.validate, design.validate, or spec.validate. Sessions are created and managed in the web app at /app/sessions. Read-only. Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan.ConnectorNo auth
- Pro/Teams: return the authenticated user's validation run history for all three lenses (architect.validate → validator='architecture', design.validate → validator='surface', spec.validate → validator='spec') with the Blueprint Readiness Score (0-100), letter grade (A-F), and tier (draft, emerging, production_ready). Each run carries a `validator` field naming its lens. Three lookup modes: (1) `run_id=<id>` returns a SINGLE run with the full persisted result_json; use this to RECOVER a result when your MCP client tool-call timed out before architect.validate, design.validate, or spec.validate returned. The run completes server-side and persists; the run_id is surfaced in the first progress notification of every validate call so you have the recovery handle even when your client gives up early. (2) `repository=<name>` returns the full per-run trend for that repository plus a regression diff between the latest two runs. (3) No arguments returns one summary per repository the user has validated, sorted by most recent. Use modes (2) or (3) BEFORE re-validating the same repository on either lens: they tell you which principles or laws regressed since the last run, so you can focus the new review on what is actually changing. Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan.ConnectorNo auth
- Pro/Teams: first-pass surface-craft review of a FRONTEND artefact (component, screen, or flow) against the 8 laws of the Experience Design Blueprint. The surface-craft companion to architect.validate: where architect.validate scores agentic ARCHITECTURE against the 10 agentic principles, design.validate scores the PERCEPTIBLE SURFACE, what the user sees, taps, scans, and remembers (Jakob's familiarity, Hick's choice load, Fitts's targets + the accessibility floor, Miller's working-memory budget, Aesthetic-Usability, Peak-End, Tesler's irreducible complexity, the Mental-Model gap). ON CLIENT TIMEOUT: DO NOT RETRY. Long-running LLM call (~60-180s at high reasoning effort, single-pass). The server mints a run_id, emits it in the FIRST progress event at t=0s (before the LLM call), and persists the run: so on a client timeout, capture that run_id and call me.validation_history(run_id='<that-id>') to fetch the persisted result instead of retrying (a retry re-runs the full 60-180s call). Runs appear in your validation-history dashboard tagged as the 'surface' dimension, distinct from the 'architecture' and 'spec' runs; pass repository to group them per project. Pass private_session=true to skip the stored run (persistence + recovery disabled); operational security + cost logs are still kept. v1 is single-pass: no certification or consensus mode yet (those stay architect.validate-only). Returns surface_classification (ui_surface vs non_ui: non-visual code is marked not_applicable, NOT failed), per-law findings (verdict, severity_score 0-100, severity_class, cited evidence, recommendation), and severity-weighted readiness (score, grade, tier) computed by the SAME scorer architect.validate uses, so all three lenses grade on one rubric. ACCESSIBILITY IS THE FLOOR: a breach of the Fitts's-Law floor (interactive target below the WCAG 2.2 24×24 minimum, missing focus visibility, an unreachable destructive confirmation) is a production_blocker, not polish. WHEN TO CALL: the user wants a craft/UX/accessibility review or a readiness grade on a frontend artefact they just built or changed. WHEN NOT TO CALL: non-visual code (backend, config, type aliases) returns tier=not_applicable, submit the actual UI surface instead. INPUTS: send the FULL artefact source verbatim as implementation_context (no truncation, no '…' placeholders, they are read as literal code). Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan. Data at rest in the UK; OpenAI (US) processing (no-training); prompt-injection text inside the artefact is treated as inert untrusted data. TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable, schema_mismatch, each carries retryable + next_action); the services raise the identical typed envelopes on this lens. CALIBRATION DISCLOSURE: the scoring prompt is a v1 first-cut mirroring the architect's contract structure; its score calibration is not yet tuned against a corpus of real runs the way architect.validate was. Treat the grade as directional craft signal, not a certified verdict. DOCTRINE: the eight laws, each law's evidence, craft-surface application, anti-patterns, and the validator questions this tool scores against, live in the `experience-design-blueprint` skill and docs/business/EXPERIENCE_DESIGN_BLUEPRINT.md (the surface-craft companion to the `architect-validation-orchestration` skill that orchestrates the agentic validators).ConnectorNo auth
- 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 <token>, Teams plan only; Pro and beta plans are refused. Data at rest in the UK.ConnectorNo auth
- 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 <token> (any plan, including basic). Returns user_email, course_slug, stages list with completion timestamps, certification block, and a next_stage hint.ConnectorNo auth
- 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 <token> (Firebase ID token, any plan). Data at rest in the UK. 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.ConnectorNo auth
- Pro/Teams: list or inspect the authenticated user's Governed Sessions (GEP-M2): durable, owner-scoped containers that group validation runs across lenses (architect.validate → 'architecture', design.validate → 'surface', spec.validate → 'spec') into one timeline for one piece of work. Two modes: (1) No arguments returns every session (id, title, status, repo_url, spec_ref, team_agents, run_count, validators = the lenses seen), newest first. (2) `session_id=<id>` returns that session plus its run timeline (light rows; fetch full results per run via me.validation_history(run_id=...)) and, for team sessions, `events` = the typed team-event log posted via me.session_event. Attach new runs by passing `session_id` to architect.validate, design.validate, or spec.validate. Sessions are created and managed in the web app at /app/sessions. Read-only. Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan.ConnectorNo auth
- Pro/Teams: return the authenticated user's validation run history for all three lenses (architect.validate → validator='architecture', design.validate → validator='surface', spec.validate → validator='spec') with the Blueprint Readiness Score (0-100), letter grade (A-F), and tier (draft, emerging, production_ready). Each run carries a `validator` field naming its lens. Three lookup modes: (1) `run_id=<id>` returns a SINGLE run with the full persisted result_json; use this to RECOVER a result when your MCP client tool-call timed out before architect.validate, design.validate, or spec.validate returned. The run completes server-side and persists; the run_id is surfaced in the first progress notification of every validate call so you have the recovery handle even when your client gives up early. (2) `repository=<name>` returns the full per-run trend for that repository plus a regression diff between the latest two runs. (3) No arguments returns one summary per repository the user has validated, sorted by most recent. Use modes (2) or (3) BEFORE re-validating the same repository on either lens: they tell you which principles or laws regressed since the last run, so you can focus the new review on what is actually changing. Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan.ConnectorNo auth
- Builds an unsigned transaction blueprint for placing a bid on a Gavel auction, including the prerequisite ERC-20 approval when the current allowance is short. You supply the auction and the repayment amount you are willing to accept; this tool validates them against live auction state and encodes the call. It does not choose an auction, a rate or a size for you — use find_auctions_matching_criteria to filter by your own criteria first. Bidding on Gavel is a reverse auction: a LOWER repayment is a more competitive bid and a lower yield to you as lender. Each bid must undercut the current best by at least the auction's bid step. Returns an unsigned transaction blueprint for the requested intent. The user is responsible for reviewing, signing, and broadcasting via their own wallet. Aletheia does not hold keys or dispatch transactions.ConnectorNo auth
- Queue invoice collection for named counterparties. This creates one durable backlog task per counterparty. The browser agent (a provider that carries a blueprint or a real portal URL) or the manual-upload route picks up each task later. Call this tool only after the user explicitly confirms the launch. Never call it on your own initiative. Get counterparty_company_ids from well_list_missing_invoices. This tool takes no period argument: a collection task belongs to a counterparty, not a month. A repeat call for a counterparty that already has a non-terminal task reuses that task (already_active: true) instead of creating a second one. provider.has_blueprint and provider.has_portal_url on an enqueued row state which counterparties a browser agent will visit (either one is enough), and which fall back to manual upload (neither). Creating the tasks launches nothing in the browser. Inside Well the tasks page and the chat card start and track them. From outside Well, hand the user the collect_url from well_preview_invoice_fetch to start the runs. That link never covers every enqueued counterparty. One link names at most 25 portals, so a counterparty past that ceiling appears in well_preview_invoice_fetch's collect_url_omits instead of on the link. A counterparty with provider: null has no portal at all and is routed to manual upload. A counterparty with an address the link cannot carry appears in collect_url_unaddressable. Never tell the user the link covers a counterparty it does not name. Use well_preview_invoice_fetch first to see what a fetch would cover — it is read-only and launches nothing. Use well_enqueue_close_invoice_fetch instead of this tool when you are inside a close run: it is the same action, scoped to that run's flow_run_id. Use this tool outside a close run. Report the counts back to the user: how many tasks were enqueued, how many of those were already active, and how many counterparties were skipped, with each skip's reason.ConnectorNo auth
- Build/extend a model from a strict Blueprint. Each item carries a single `name` (visible label + formula identifier). Names must be unique within the model (case-insensitive). Callup items omit `name` — they inherit it from `source`. Multi-word names use back-ticks in formulas (e.g. `` `Annual Revenue` * 12 ``). Use this for bulk creation of whole sections with their hierarchy; for incremental edits on a single item or two, prefer layerz_patch (which also supports section+children in one batch via temp `id`/`parent`). Recursive time-series (roll-forwards, cumulative trackers, indexation) use the lag suffix `<name>_M-N` / `<name>_Q-N` / `<name>_Y-N` (and `<name>_M-$var` for dynamic lag). Self-referencing lag is allowed (`A = A_M-1 + delta`) — only zero-lag self-reference (`A = A + …`) is rejected. Cross-granularity: `_Y-1` on a monthly item = 12 periods, `_Q-1` on monthly = 3, `_Y-1` on quarterly = 4. At period 0 a lagged ref returns 0 (or `opening_balance` for balance items). For canonical BOP/EOP patterns prefer a `balance` item with `opening_balance` and children for the period deltas. Alternatively pass `template_id` (mutually exclusive with `blueprint`, discover via layerz_list_templates) to apply a stored template as a module: its blueprint is loaded server-side and merged in. After applying, read the template guide (its `description`, via layerz_get_template) and update the model FINANCE.md (layerz_set_finance_md) to match the project. Not available for read-only API keys.ConnectorDestructiveOAuth
- Browse proven ad formula blueprints — structural patterns clustered from 3-10+ winning ads that independently converged on the same beat architecture while Meta kept rewarding them with sustained spend. Takes optional filters: vertical, creative_format (e.g. TALKING_HEAD, UGC, FOUNDER_STORY), marketing_angle, algo_intent, hook_type, and limit (1-10, default 5). Each formula returns: source ad count, average active days (runtime proof), confidence score, 6-layer beat blueprint, per-beat visual direction, marketing angle, psychology mission. Free, read-only, idempotent. Use this when the user asks "what's working in [category]", "show me formulas for talking-head ads", "what scripts work in my vertical", or wants category-level pattern discovery before committing to a single ad. Pass the returned formula id to generate_adscript with source_type="formula" for synthesis. When choosing among results: prioritise (1) avg_active_days as primary proof, (2) marketing_angle alignment with the brand's buyer tension, (3) source_ad_count for cluster robustness, (4) confidence_score as tiebreaker. Do NOT use when the user names a specific ad — decode that ad with decode_ad. Do NOT use for sentence-level transcript fidelity — formulas abstract the structure, not exact copy.ConnectorNo auth
- Generate direct-response video ad scripts by fusing a proven structural source (decoded ad or formula) with a brand's PowerSource. Output is feed-native ad copy for paid social (Meta, TikTok, Reels) in the brand's voice — hook, beat-by-beat body, CTA close, plus visual direction per beat. Takes source_id (from adformula_intelligence, decoder_intelligence, or decode_ad), source_type ("formula" or "decode"), powersource_id (from any create_powersource_*), and tunable params: count (1-5 variants, tensions and selling points auto-rotated across variants), script_mode ("blueprint" preserves source structure exactly, "remix" preserves psychology but writes original copy), duration (target seconds), audience, tension override, selling_points override, voice_mode ("creator" for UGC default, "brand" for owned channels), and idempotency_key. Use this when the user says "write me a script", "I need a TikTok script", "write an ad based on this", or wants shell-faithful replication of a proven winner in their own brand voice. REQUIRES both a structural source AND a powersource — guide the user through creating either if missing. Metered pricing — typically 2-5 credits per script (~2 credits for 15s, ~5 credits for 60s). Pre-flight reserves a 17-credit ceiling and refunds the difference after measurement. Do NOT use to discover sources — use decoder_intelligence or adformula_intelligence first. Do NOT use to extract brand intel — use create_powersource_url first.ConnectorNo auth