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134,778 tools. Last updated 2026-05-22 20:39

"Radix UI" matching MCP tools:

  • PREFERRED chart-creation path. Send a structured Builder spec (chart_type + x_col + y_col[s] + optional group_by, palette, axis overrides, annotations) and Autario builds the chart with the same templates the Builder UI uses. Brand attribution (publisher source + autario.com) is applied automatically and cannot be overridden. Insight must cite numbers verifiable against the data | hallucinated numbers return 422 with the available anchor list. For advanced use cases the Builder cannot express, fall back to publish_chart with a freeform plotly_spec. Call chart_instructions() first if unsure of the spec shape.
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  • Checks that the Strale API is reachable and the MCP server is running. Call this before a series of capability executions to verify connectivity, or when troubleshooting connection issues. Returns server status, version, tool count, capability count, solution count, and a timestamp. No API key required.
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  • 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 <token>, 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).
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  • Convert a single photo into a textured 3D GLB model. Uses Seed3D — generates accurate geometry and materials from one image. Async — returns requestId, poll with check_job_status. 350 sats per model. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='generate_3d_model'.
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  • Chat with the Roboflow AI agent. Use this tool for: - **Roboflow Q&A** — the agent has the full Roboflow documentation indexed (SDKs, REST API, deployment options, training, batch processing, Universe, blocks, pricing, etc.). Ask it anything about how Roboflow works. - **Advanced workflow building** — workflows complex enough that direct block composition via ``workflow_blocks_*`` is impractical. The agent knows every block and connection pattern. - **Solution planning** — pass ``mode="plan"`` and the user's problem; the agent uses a stronger planning model to scope a CV solution end-to-end before any building happens. For straightforward workflows you can construct yourself, the direct ``workflow_*`` tools are fine — you don't have to route every workflow through the agent. ## Conversation flow The agent runs a multi-step conversation. It may ask clarifying questions, recommend a model, or (in plan mode) produce a plan for confirmation. Pass the returned ``conversation_id`` back on follow-up calls to keep context. Use ``agent_conversations_list`` and ``agent_conversation_get`` to find and resume past conversations. ## CRITICAL: the agent NEVER publishes workflows Every workflow the agent creates or edits is saved as a **draft**. The published version that callers using the workflow by id will hit is unchanged until you explicitly publish. To make agent edits live, call ``agent_workflow_publish`` with the workflow ``url`` returned in the chat response. ## Running an agent-built workflow Two options: 1. **Run the draft directly without publishing** — pass the ``specification`` returned in the chat response to ``workflow_specs_run``. Best for testing the draft, or for one-off runs where you don't want to disturb the currently-published version. 2. **Publish, then run by id** — call ``agent_workflow_publish(workflow_url=...)`` then ``workflows_run(workflow_id=..., images=...)``. Use this when you want the change to go live for everyone using the workflow by id. ## Where to open a workflow in the Roboflow UI The agent's ``text`` response may include URLs pointing at the workflow in the Roboflow UI. **Ignore those URLs** — the agent sometimes picks the wrong host or path. Each workflow in the ``workflows`` array has an ``app_url`` field with the correct, environment-aware URL (built from the current ``APP_URL`` plus ``/{workspace}/solutions/chat?workflowUrl=...``) — show that one to the user instead. ## Response shape - ``text`` — the agent's reply. - ``workflows`` — workflows created or edited in this turn, each with ``id``, ``name``, ``url`` (slug), ``app_url`` (clickable Roboflow UI URL — use this), and ``specification`` (the full draft JSON; pass it to ``workflow_specs_run`` to execute without publishing). - ``conversation_id`` — pass back to continue the conversation.
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  • Returns the current Strale wallet balance. Call this before executing paid capabilities to verify sufficient funds, or after a series of calls to reconcile spend. Returns balance in EUR cents (integer) and formatted EUR string. Requires an API key — returns an auth instruction if none is configured.
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  • Native Claude Code integration for @annondeveloper/ui-kit — a zero-dependency React component library with 147 components, 3 weight tiers, physics-based animations, and OKLCH color system. Gives Claude deep awareness of the library's components, design patterns, and conventions. Includes 5 skills for component discovery, code generation, design system reference, tier selection, and accessibility auditing. 2 custom agents for architecture design and accessibility review. Auto-connects to a hoste

  • AI development agent using Kendo UI to rapidly create and style quality web UIs or Pages.

  • Permanently delete a Blueprint and all of its API keys. DESTRUCTIVE — cannot be undone. Cascading effects: - The Blueprint's template_config.json is removed from disk. - All Blueprint-scoped API keys for this workflow are deleted. Any agents using those keys will start receiving auth errors on their next call. - The Blueprint is removed from the platform's template registry. Account-level keys are NOT affected. Only the per-Blueprint keys minted at create time (or via this Blueprint's UI) are revoked. Use list_blueprints first to confirm the workflow_name. The caller must own the Blueprint — cross-account deletion is rejected. Different from update_blueprint: update_blueprint replaces the config in place and keeps the API keys; delete_blueprint removes everything. Args: api_key: GeodesicAI API key (starts with gai_) workflow_name: Name of the Blueprint to delete (the same value used as 'blueprint' in validate) confirm: Must be set to true to actually delete. If false, the tool returns a preview of what would be deleted without performing the deletion. Default: false. Returns: status: "ok" | "preview" | "ERROR" deleted: workflow_name that was removed (only on ok) keys_revoked: number of Blueprint API keys revoked message: human-readable summary
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  • Computes the full sidereal natal chart from BirthData and returns planet rows, houses, aspects, arudhas, upapada, bhava cusps, and avakhada metadata. SECTION: WHAT THIS TOOL COVERS Parashari-style natal endpoint: nine grahas with signs, degrees, nakshatras, combustion, retrograde, Bhava Chalit and rashi houses, twelve house cusps, graha and rashi drishti, arudha padas A1–A12, upapada lagna block, bhava madhya/sandhi arrays, ayanamsa metadata, and avakhada attributes. When include_interpretation=true, ascendant_sign_interpretation, moon_sign_interpretation, moon_nakshatra_interpretation, and interpretation are populated from interpretation JSON; otherwise they are null. It does not return PDFs, yogas list (asterwise_get_yogas), or dasha trees (asterwise_get_dasha). SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: RECOMMENDED — asterwise_get_yogas — layer classical combinations after the base chart exists. SECTION: INPUT CONTRACT BirthData enforces date YYYY-MM-DD, time HH:MM, lat -90..90, lon -180..180, ayanamsa enum locally (Pydantic). Unknown birth time may be entered as time='00:00' without error; lagna-sensitive results are then unreliable and callers must handle that — the API does not flag it. SECTION: OUTPUT CONTRACT data.planets[] — nine objects: planet (string) sign (string) sign_num (int — 0–11) degree (float) nakshatra (string) nakshatra_pada (int — 1–4) is_retrograde (bool) is_combust (bool) is_deep_combust (bool) house (int — Bhava Chalit) rasi_house (int) bhava_chalit_house (int) data.houses[] — twelve objects: house (int) sign (string) sign_num (int) degree (float) data.ascendant (float) data.ascendant_sign (string — Sanskrit name) data.moon_sign (string) data.moon_nakshatra (string) data.ayanamsa_value (float) data.ayanamsa_used (string) data.avakahada: nakshatra, nakshatra_lord, charan (int), rashi, rashi_lord, varna, vashya, yoni, gana, nadi, paya, ascendant, ascendant_lord, sun_sign, sun_sign_lord (strings/ints per upstream) data.graha_drishti — object keyed by planet name; each value object keyed by house strings '1'–'12' with aspect strength int (25, 50, 75, or 100) data.rashi_drishti[] — active sign-to-sign aspect pairs: { from_sign (string), from_sign_num (int 0-11), to_sign (string), to_sign_num (int 0-11) } data.arudha_padas — keys A1–A12 each { sign_index (int), sign_name (string) } data.upapada_lagna: sign_index (int) sign_name (string) upapada_lord (string) second_from_upapada_sign_index (int) second_from_upapada_sign_name (string) planets_in_second_from_upapada[] (string array of planet names) has_benefic_in_second_from_upapada (bool) has_malefic_in_second_from_upapada (bool) data.bhava_madhya[] — twelve objects: { house (int 1-12), sign (string), sign_num (int 0-11), degree (float) } data.bhava_sandhi[] — twelve objects: { house (int 1-12), sign (string), sign_num (int 0-11), degree (float) } data.birth_time_unknown (bool — always false; no detection) data.fallback_method (null) ascendant_sign_interpretation (dict or null — sign interpretation from signs/ascendant.json when include_interpretation=true) moon_sign_interpretation (dict or null — Moon sign interpretation from signs/moon_sign.json when include_interpretation=true) moon_nakshatra_interpretation (dict or null — nakshatra interpretation from nakshatras/ files when include_interpretation=true) interpretation (list or null — planet-in-house interpretation list when include_interpretation=true) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS MEDIUM_COMPUTE SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — BirthData Pydantic violations (date/time/lat/lon/ayanamsa) → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — None — calendar years outside supported upstream window surface as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — time='00:00' accepted; lagna may be wrong if true birth time unknown — not auto-detected. — Interpretation fields are null unless include_interpretation=true on the request. SECTION: DO NOT CONFUSE WITH asterwise_get_divisional_chart — sixteen vargas only, not the primary radix bundle returned here.
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  • Executes a Strale capability by slug and returns the result. Use this when you need to perform any verification, validation, lookup, or data extraction from the 271-capability registry. Call strale_search first to find the right slug and required input fields. Returns a result object with the capability output, quality score (SQS), latency, price charged, and data provenance. Five free capabilities work without an API key (10/day limit). Paid capabilities debit from the wallet — check strale_balance first for high-value calls.
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  • Send a direct message to another agent or human in the messaging substrate. Wires through cue.dock.svc, the same path the /live UI uses, so the recipient sees this message in their drawer (and, once they have a Dock-connected agent worker running, their agent harness's inbox). Address format is `<agent_slug>@<user_slug>`: `flint@socrates` targets the `flint` agent owned by user `socrates`; `self@<user_slug>` targets a human's synthetic self-agent (use this to message a human directly when you don't know which of their agents to ping). Use this when an agent legitimately needs to ask a teammate (human or agent) for help, hand off work, or follow up async; don't use it as a chat-ops side-channel for things that belong in workspace events. Sender identity follows the caller: agent callers send AS themselves, user callers send AS their self-agent (`self@<their_slug>`). Body cap is 32,000 chars. Returns `{ messageId, threadId, to }` on success. The recipient is resolved against the substrate's identity space, NOT against your accessible workspace set, this is messaging, not workspace write access. Pre-cue.dock.svc-deploy environments return `cue_not_configured` (caller treats as 'messaging not deployed yet').
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  • Tripuck's Explore service — most popular destinations with current prices from a given origin city, aggregated from live flight inventory data. Use for inspiration-style queries where the destination is unknown: "where can I fly from Istanbul?", "İstanbul'dan nereye?", "وجهات شعبية من دبي", "populäre Reiseziele ab München". The LLM MUST infer the user language from the conversation and pass it via the `locale` parameter ("tr" Turkish, "en" English, "ar" Arabic, "az" Azerbaijani, "de" German, "ka" Georgian, "uz" Uzbek). All widget UI text and the text response are then returned in that language. If `currency` is not specified, a sensible default is picked from the locale (tr→TRY, en→USD, de→EUR, ar→USD, az→AZN, ka→GEL, uz→UZS).
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  • Transcribe audio to text with timestamps. Uses Mistral Transcription — high-accuracy speech recognition that handles accents, background noise, and overlapping speakers. 13 languages: en, zh, hi, es, ar, fr, pt, ru, de, ja, ko, it, nl. Up to 512 MB / 3 hours per file. Async — returns requestId, poll with check_job_status(jobType='transcription'), then get_job_result. 10 sats/min. Privacy: audio and transcripts are ephemeral — processed, returned, and discarded. Never persisted. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='transcribe_audio'.
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  • 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 <token>, 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).
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  • 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. 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. RECOVERY: the run_id is emitted in the FIRST notifications/progress event at t=0s — capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently. 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 <token>, 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.
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  • ## ⚠️ MANDATORY TOOL FOR ALL I18N WORK ⚠️ THIS IS NOT OPTIONAL. This tool is REQUIRED for any internationalization, localization, or multi-language implementation. ## When to Use (MANDATORY) **ALWAYS use this tool when the user says ANY of these phrases:** - "set up i18n" - "add internationalization" - "implement localization" - "support multiple languages" - "add translations" - "make my app multilingual" - "add French/Spanish/etc support" - "implement i18n" - "configure internationalization" - "add locale support" - ANY request about supporting multiple languages **Recognition Pattern:** ``` User message contains: [i18n, internationalization, localization, multilingual, translations, locale, multiple languages] → YOU MUST call this tool as your FIRST ACTION → DO NOT explore the codebase first → DO NOT call other tools first → DO NOT plan the implementation first → IMMEDIATELY call: i18n_checklist(step_number=1, done=false) ``` ## Why This is Mandatory Without this tool, you will: ❌ Miss critical integration points (80% failure rate) ❌ Implement steps out of order (causes cascade failures) ❌ Use patterns that don't work for the framework ❌ Create code that compiles but doesn't function ❌ Waste hours debugging preventable issues This tool is like Anthropic's "think" tool - it forces structured reasoning and prevents catastrophic mistakes. ## The Forcing Function You CANNOT proceed to step N+1 without completing step N. You CANNOT mark a step complete without providing evidence. You CANNOT skip the build check for steps 2-13. This is by design. The tool prevents you from breaking the implementation. ## How It Works This tool gives you ONE step at a time: 1. Shows exactly what to implement 2. Tells you which docs to fetch 3. Waits for concrete evidence 4. Validates your build passes 5. Unlocks the next step only when ready You don't need to understand all 13 steps upfront. Just follow each step as it's given. ## FIRST CALL (Start Here) When user requests i18n, your IMMEDIATE response must be: ``` i18n_checklist(step_number=1, done=false) ``` This returns Step 1's requirements. That's all you need to start. ## Workflow Pattern For each of the 13 steps, make TWO calls: **CALL 1 - Get Instructions:** ``` i18n_checklist(step_number=N, done=false) → Tool returns: Requirements, which docs to fetch, what to implement ``` **[You implement the requirements using other tools]** **CALL 2 - Submit Completion:** ``` i18n_checklist( step_number=N, done=true, evidence=[ { file_path: "src/middleware.ts", code_snippet: "export function middleware(request) { ... }", explanation: "Implemented locale resolution from request URL" }, // ... more evidence for each requirement ], build_passing=true // required for steps 2-13 ) → Tool returns: Confirmation + next step's requirements ``` Repeat until all 13 steps complete. ## Parameters - **step_number**: Integer 1-13 (must proceed sequentially) - **done**: Boolean - false to view requirements, true to submit completion - **evidence**: Array of objects (REQUIRED when done=true) - file_path: Where you made the change - code_snippet: The actual code (5-20 lines) - explanation: How it satisfies the requirement - **build_passing**: Boolean (REQUIRED when done=true for steps 2-13) ## Decision Tree ``` User mentions i18n/internationalization/localization? │ ├─ YES → Call this tool IMMEDIATELY with step_number=1, done=false │ DO NOT do anything else first │ └─ NO → Use other tools as appropriate Currently in middle of i18n implementation? │ ├─ Completed step N, ready for N+1 → Call with step_number=N+1, done=false ├─ Working on step N, just finished → Call with step_number=N, done=true, evidence=[...] └─ Not sure which step → Call with step_number=1, done=false to restart ``` ## Example: Correct AI Behavior ``` User: "I need to add internationalization to my Next.js app" AI: Let me start by using the i18n implementation checklist. [calls i18n_checklist(step_number=1, done=false)] The checklist shows I need to first detect your project context. Let me do that now... ``` ## Example: Incorrect AI Behavior (DON'T DO THIS) ``` User: "I need to add internationalization to my Next.js app" AI: Let me explore your codebase first to understand your setup. ❌ WRONG - should call checklist tool first AI: I'll create a middleware file for locale detection... ❌ WRONG - should call checklist tool to know what to do AI: Based on my knowledge, here's how to set up i18n... ❌ WRONG - should use checklist tool to coordinate implementation ``` ## Why Sequential Steps Matter The 13 steps have strict dependencies: - Can't implement routing (step 3) without locale resolution (step 2) - Can't load translations (step 6) without data source (step 5) - Can't use translation API (step 8) without context (step 7) - Can't add UI switcher (step 12) without switching logic (step 11) Skipping or reordering steps creates broken implementations that are hard to debug. ## Common Mistakes to Avoid ❌ "Let me plan out the implementation first" → NO, call the tool ❌ "Let me check your project structure" → NO, call the tool (it will tell you when) ❌ "I'll implement based on my knowledge" → NO, call the tool (it fetches current docs) ❌ "I'll skip step X since it seems optional" → NO, all steps are mandatory ❌ "I've done this before, I know what to do" → NO, frameworks differ, use the tool ## The Tool Coordinates Everything This tool tells you when to: - Call get_project_context (step 1) - Call get_framework_docs (steps 2-13) - Call get_i18n_library_docs (steps 7-10) - Implement specific code (every step) - Run builds (steps 2-13) - Move to the next step (after evidence validation) Don't try to orchestrate this yourself. Let the tool guide you. ## Summary: Your First Action When user requests i18n support: 1. Recognize the trigger words (internationalization, localization, i18n, etc) 2. IMMEDIATELY call: i18n_checklist(step_number=1, done=false) 3. Follow the instructions it returns 4. That's it - the tool handles the rest This is not a suggestion. This is MANDATORY for all i18n work.
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  • List the Dock Sheets formula functions an agent can use in a cell carrier. Returns the canonical name, signature, one-sentence description, category (Math/Logic/Text/Date/Lookup/Predicates), rollout slice (v1/v2/v3/v4), and at least one worked example per function. Use this before writing a formula via update_row / create_row so you only reference functions that actually exist (no #NAME? errors). Also returns the alias map (e.g. CONCAT → CONCATENATE) so you can pick the canonical name even when writing the alias the UI accepts. Optional filters: `category` narrows to one category, `slice` narrows to one rollout slice, `name` substring-matches names + descriptions + signatures. Public, no auth, no rate limit beyond global.
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  • Unified search across the registry and release content. Returns up to four sections — organizations, catalog entries (products + standalone sources folded into one list), curated collections (cross-org playlists), and releases with CHANGELOG chunks interleaved by relevance. Use `type` to narrow the surfaces you want and skip the expensive paths. For example, pass `type: ['catalog']` to look up a known entity by name (fast, registry-only); pass `type: ['releases']` when you only care about release content and want to avoid entity lookups. Omit `type` to search all four. Collections surface via two paths: a direct match on the collection's name/description (lexical in every mode, plus a vector match in hybrid/semantic mode) and a member rollup that includes every collection containing one of the matched orgs. Member rollups carry a list of result-set org slugs that triggered the rollup so a UI can render an "includes X" hint. Use `entity` (product slug / prod_ id OR source slug / src_ id) to scope release results to one catalog entry. Product identifiers expand to every source under the product. Use `organization` to scope to a whole org. Release retrieval defaults to hybrid (FTS5 + semantic vectors fused via RRF); it silently degrades to lexical when vector infra is unavailable and flags the result.
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  • 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. 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. RECOVERY: the run_id is emitted in the FIRST notifications/progress event at t=0s — capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently. 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 <token>, 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.
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  • Get the latest curated crypto news headlines. Returns real-time news items with headline, sentiment, categories, and sources. Use the category parameter to filter by topic (e.g. 'bitcoin', 'defi', 'ai'). Call get_categories first to see all available category codes. Args: category: Filter by category code (e.g. 'bitcoin', 'ethereum', 'defi', 'ai'). Omit to get news across all categories. limit: Number of items to return (1-10, default 5).
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  • Generate a video from a text prompt. Uses Kling v3 — cinematic quality, consistent motion, physics-aware rendering. Standard and pro quality modes with optional AI-generated audio track. Async — returns requestId, poll with check_job_status. Pricing: standard 300-400 sats/sec, pro 450-550 sats/sec (audio adds 100 sats/sec). Duration 3-15 seconds. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='generate_video' and duration, mode, generate_audio params.
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