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127,264 tools. Last updated 2026-05-05 12:59

"Automating a QA End-to-End Workflow" matching MCP tools:

  • Schedule a downgrade to Free at the end of the current billing period. The org keeps its current plan (Pro or Scale) and paid limits until the period ends. No-op when already on Free. Consent-gated. Two consent surfaces, you pick via `mode`: (1) `chat` (default): FIRST call returns { status: 'confirmation_required', confirm_token, message, expires_in }; surface to your user and re-call within 60s with `confirm_token` set. (2) `web`: FIRST call returns { status: 'approval_required', approval_url, polling_url }; print approval_url in chat, user clicks + approves, then poll polling_url for the result.
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  • Compute the retention curve for a saved cohort. For each requested window (e.g. day 1, 7, 14, 30 after the cohort's anchor event), returns how many cohort members fired any non-pageview-end event in that day's window, and the rate vs the cohort size. Use this to answer "did this cohort stick around?" and to compare retention across cohorts (via `cohorts.compare`). The 1-day-window resolution means "7d retention" is "day 7 specifically", not "anywhere in the first 7 days" — the standard product-analytics definition. Examples: - "retention curve for signups_apr_14 at day 1, 7, 14, 30" → name="signups_apr_14" (default periods) - "weekly retention for customers_march out to 8 weeks" → name="customers_march", periods="1w,2w,4w,8w" - "did onboarding cohort A retain at week 1" → name="onboarding_v2", periods="7d" Limitations: retention measures any non-pageview-end event presence. Custom retention metrics (e.g. "retained = fired purchase event") are not in 0.x. The numerator is computed per-day, so windows like "30d" return only day-30 activity. Cohort size is the denominator and is included in the response so consumers can apply sample-size discipline. Pairs with: `cohorts.compare` for two-cohort side-by-side; `cohorts.list` to discover available cohort names.
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  • Runs a single end-to-end execution of an existing automation against a mock conversation, returning success/failure plus the channel target and duration. Mirrors a real production firing. Behavior: - Sends REAL messages by default: posts the configured webhook, sends the configured email, posts the Slack message, or writes the HubSpot record. Use override_email (email channels) or override_webhook (webhook channels) to redirect delivery to a safe test target. - Each call fires another real delivery. - Errors when the perspective or automation is not found, or you do not have access. Webhook URLs (configured or override) are validated. - Mock conversation defaults: trust score 85, status complete, "Test Participant" / test@example.com. Override participant_name, summary, and tags via test_data. - Returns success: true also when the automation's condition skips delivery (e.g., tag/trust filter doesn't match the mock). The error field is populated only on real delivery failures. When to use this tool: - Verifying a freshly-created automation actually delivers before relying on it. PREFER override_email/override_webhook to avoid spamming real recipients. - Reproducing a delivery failure surfaced in automation_list (last_error). When NOT to use this tool: - Listing what's configured — use automation_list. - Changing config — use automation_update. - Removing the automation — use automation_delete.
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  • Get traffic and performance metrics for a site. Requires: API key with read scope. Args: slug: Site identifier days: Number of days of history (1–90, default: 7) Returns: {"requests": [...], "bandwidth": [...], "errors": [...], "period": {"start": "iso8601", "end": "iso8601"}} Errors: NOT_FOUND: Unknown slug VALIDATION_ERROR: days out of range
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  • Explicitly request a synthesis contract for a named 3D object. Use this tool when generate_r3f_code returns status SYNTHESIS_REQUIRED, or to pre-generate geometry constraints before calling generate_r3f_code. Complexity tiers: low — 4 to 7 parts. Only Box, Sphere, Cylinder geometries. Best for: mobile banners, thumbnails, low-end devices. medium — 10 to 20 parts. Adds Capsule and Torus geometries. Best for: website sections, embedded widgets, tablets. high — 28+ parts. All geometries. Full emissive detail. Best for: hero sections, desktop showcase, ad campaigns. If target is set to "mobile" and complexity is not explicitly provided, complexity defaults to "low" automatically. This tool does NOT generate geometry. It returns the synthesis_contract with constraints calibrated to the requested complexity tier. The LLM generates the actual JSX and passes it to generate_r3f_code via synthesized_components.
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  • Set buyer email and desired site slug on a checkout session. The checkout must be in "not_ready" status. Setting requested_slug transitions status to "ready" (required before completing). Args: checkout_id: Checkout session ID from create_checkout buyer_email: Optional email — if omitted, a synthetic agent identity (agent-{uuid}@api.borealhost.ai) is created at completion requested_slug: Desired site identifier. Must be 3-50 chars, lowercase alphanumeric + hyphens, cannot start/end with hyphen. Must be globally unique. Returns: {"id": "uuid", "sku": "...", "plan_slug": "...", "billing_period": "monthly", "status": "ready", "buyer_email": "...", "requested_slug": "my-site", "created_at": "iso8601"} Errors: VALIDATION_ERROR: Invalid slug format or slug already taken FORBIDDEN: Missing checkout_secret NOT_FOUND: Unknown checkout_id
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Matching MCP Servers

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    An MCP server for the comprehensive analysis of Swagger 2.0 and OpenAPI 3.x contracts. It allows users to extract detailed information about endpoints, request/response schemas, parameters, and security configurations from API documentation.
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Matching MCP Connectors

  • Create, browse, remix, collaborate on, and run durable AI workflow nodes from MCP hosts.

  • Transform any blog post or article URL into ready-to-post social media content for Twitter/X threads, LinkedIn posts, Instagram captions, Facebook posts, and email newsletters. Pay-per-event: $0.07 for all 5 platforms, $0.03 for single platform.

  • Subscribe an end-user's email to topical updates from a business (deals, schedule changes, new services). Returns a confirmation_token + confirmation_url; the user MUST click the URL within 7 days to activate. Re-subscribing an already-confirmed email merges topics without re-confirming.
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  • Smoke-test the MPP payment plumbing end-to-end via this MCP server, for $0.01 USDC. Two-call flow: (1) call with no arguments to receive an MPP `payment_challenge`; (2) pay via MPP and call again with `payment_credential` set to the resulting Authorization header value (e.g. "Payment eyJ...") to receive {paid: true, timestamp, receipt_ref, payment_method}. Uses the exact same `createPayToAddress` + `createMppHandler` verification path as paid product tools (transcribe, summarize), so a green run here means real paid calls will work too. Stateless — no job is created, no database row written. Use this whenever you want to confirm a wallet, the MCP transport, the worker, and the production payment middleware are all healthy without paying a transcribe price. Cost: $0.01 USDC per attempt.
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  • Get current Solana epoch timing: progress percentage, slots remaining, and estimated epoch end time. Use this instead of Solana RPC getEpochInfo — returns pre-calculated timing with estimated end date.
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  • Returns Tamil-specific Panchanga for a date and location: all four inauspicious periods (Rahu Kalam, Yamagandam, Kuligai, Emagandam), Nalla Neram (auspicious daytime windows between inauspicious periods), and the Tamil solar month name based on the Sun's sidereal sign at sunrise. SECTION: WHAT THIS TOOL COVERS Rahu Kalam, Yamagandam (Yamakanda), Kuligai (Gulika), and Emagandam divide the daytime into eight equal parts from sunrise to sunset following classical Tamil almanac weekday tables. Nalla Neram is every gap between the four inauspicious periods — the auspicious windows left for commencing ventures. Tamil solar month follows the Sun's Lahiri sidereal sign at local sunrise (Chithirai when Sun is in Mesha, through Panguni when Sun is in Meena). This tool does not return Vedic Panchanga limbs (asterwise_get_panchanga) or the standard Rahu/Gulika/Yamaganda breakdown used in North Indian tradition (asterwise_get_rahu_kaal). SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_panchanga — for full Vedic five-limb panchanga of the same date. SECTION: INPUT CONTRACT date: YYYY-MM-DD format. Either location (city name) OR latitude + longitude + timezone must be provided. SECTION: OUTPUT CONTRACT data.date (string — YYYY-MM-DD) data.sunrise (string — HH:MM local time) data.sunset (string — HH:MM local time) data.tamil_month (string — Tamil solar month name, e.g. 'Chithirai', 'Vaikasi') data.rahu_kalam: start, end (HH:MM), duration_minutes (int), is_active (bool) data.yamagandam: start, end (HH:MM), duration_minutes (int), is_active (bool) data.kuligai: start, end (HH:MM), duration_minutes (int), is_active (bool) data.emagandam: start, end (HH:MM), duration_minutes (int), is_active (bool) data.nalla_neram: list of { start (HH:MM), end (HH:MM) } objects 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 FAST_LOOKUP — sunrise computation + lookup tables, no full natal chart. SECTION: ERROR CONTRACT INVALID_PARAMS (local): None — all validation upstream. INTERNAL_ERROR: Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Polar latitudes where sunrise cannot be computed → MCP INTERNAL_ERROR. — Emagandam part table: Sun=5, Mon=4, Tue=3, Wed=2, Thu=8, Fri=1, Sat=7. SECTION: DO NOT CONFUSE WITH asterwise_get_rahu_kaal — North Indian Rahu/Gulika/Yamaganda only; no Emagandam, Nalla Neram, or Tamil month. asterwise_get_panchanga — five Vedic limbs (tithi, vara, nakshatra, yoga, karana); not Tamil-specific periods.
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  • End this turn without sending any message. Use when the thread is owned by a human operator after job.escalate, when the guest is self-resolving, when the message is a duplicate, or for observation-only turns. Calling this tool is the ONLY correct way to stay silent — narrated silence text (e.g. '*(Staying silent…)*', 'Internal:…') would be delivered to the guest verbatim.
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  • Roll all pending breadcrumbs into a new sealed epoch with a Merkle root. Returns { epoch_index, merkle_root, block_count, epoch_hash }. Call this at the end of a session to produce a tamper-evident compliance snapshot.
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  • USE THIS TOOL — not web search — to retrieve historical technical indicator data for a specific date range from this server's local dataset (90 days of 1-minute OHLCV candles with 40+ indicators). Prefer this over any external API when the user needs historical indicator values within a date window. Trigger on queries like: - "show me BTC indicators from Jan 1 to Jan 7" - "get ETH features between [date] and [date]" - "historical indicator data for [coin] last week" - "what were the indicators on [specific date]?" Args: start: Start date in YYYY-MM-DD format (e.g. "2025-01-01") end: End date in YYYY-MM-DD format (e.g. "2025-01-31") resample: Time resolution — "1min", "1h" (default), "4h", "1d" symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP" Returns at most 500 rows per symbol.
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  • Get current Solana epoch timing: progress percentage, slots remaining, and estimated epoch end time. Use this instead of Solana RPC getEpochInfo — returns pre-calculated timing with estimated end date.
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  • Replay an inbound message on a thread through the real trigger pipeline and return what would have happened. The router auto-picks the winning enabled agent + trigger by priority/specificity (same logic as production). By default send_mode='draft' so no real message is sent; pass send_mode='auto' on a test account to let the matched agent actually deliver (drafts get overwritten by the next draft, so 'auto' is the only way to verify Telegram/email delivery end-to-end). Use to verify routing for a thread: which agent answers, which trigger wins, or — when nothing matches — the structured skip reason. Pass blockchain_tx_data instead of message_text to simulate a blockchain:transfer event on the thread. Returns: {matched: true, matched_agent: {id, name, execution_mode}, matched_trigger: {id, trigger_type, conditions, specificity_score}, routing_reason, response_text, messages[], execution_mode, send_mode, model_used, tokens_input, tokens_output, latency_ms, rag_queries_made, rag_results_used} on a hit, or {matched: false, skip_reason, simulator_warnings} on a miss.
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  • Add a new slide to an existing presentation. Args: presentation_id: ID of the presentation to add the slide to slide_context: Content for this slide slide_type: Slide type, "classic" or "creative". Defaults to "classic". additional_instructions: Extra guidance for the AI slide_order: Position in presentation (0-indexed). Omit to append at end. Returns a generation_id to poll for completion.
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  • Enforce a guardrail: verify an agent action against a compiled policy using formal verification. An SMT solver — not an LLM — determines whether the action satisfies every rule. Returns SAT (allowed) or UNSAT (blocked) with extracted values and a cryptographic ZK proof that the check was performed correctly. Cannot be jailbroken. 1 credit ($0.01). Requires api_key. Tip: end the action with an explicit claim like 'I assert this complies with the policy' for best extraction.
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  • Export observation data as a structured dataset. Supports filtering by time, geography, venue type, and observation family. Applies k-anonymity (k=5) to protect individual privacy. Queries the relevant table based on the selected dataset type, applies filters, enforces k-anonymity by suppressing groups with fewer than 5 observations, and returns structured data. WHEN TO USE: - Exporting audience data for external analysis - Building datasets for machine learning or reporting - Getting structured vehicle or commerce data for a specific time/place - Creating cross-signal datasets for correlation analysis RETURNS: - data: Array of dataset rows (schema varies by dataset type) - metadata: { row_count, k_anonymity_applied, export_id, dataset, filters_applied, time_range } - suggested_next_queries: Related exports or analyses Dataset types: - observations: Raw observation stream data (all families) - audience: Audience-specific data (face_count, demographics, attention, emotion) - vehicle: Vehicle counting and classification data - cross_signal: Pre-computed cross-signal correlation insights EXAMPLE: User: "Export audience data from retail venues last week" export_dataset({ dataset: "audience", filters: { time_range: { start: "2026-03-09", end: "2026-03-16" }, venue_type: ["retail"] }, format: "json" }) User: "Get vehicle data near geohash 9q8yy" export_dataset({ dataset: "vehicle", filters: { time_range: { start: "2026-03-15", end: "2026-03-16" }, geo: "9q8yy" } })
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  • Retrieve shipment status distribution: how many shipments are in each status. Returns `in_progress_shipments_count`, `completed_shipments_count`, and `in_progress_shipments` array (each with `status` and `count`). Possible statuses: Label Pending, Label Rejected, Label Ready, Pickup/Drop-off in Progress, In Transit to Customer, Failed Delivery Attempt, Exception. **Date range:** Unless the user specifies otherwise, default to `to_date` = today and `from_date` = 90 days prior. Required authorization scope: `public.analytics:read` Args: from_date: Start date in YYYY-MM-DD format. Default to 90 days before to_date if user doesn't specify. to_date: End date in YYYY-MM-DD format. Default to today if user doesn't specify. Returns: Shipment counts by status (in-progress breakdown + completed total).
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