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250,543 tools. Last updated 2026-06-30 02:49

"namespace:org.bull-run" matching MCP tools:

  • me.validation_history

    AI Design Blueprint Doctrine

    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 <token>. Pro or Teams plan required.
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  • retry_run

    Hydrata - ANUGA Flood Simulation

    Retry a failed simulation run. Resets an errored run back to 'created' status and triggers a new package build. The same run ID is reused. Only valid when status is 'error'. Returns 409 for any other state.
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  • get_store_flow_errors

    Power Automate MCP Server by Flow Studio

    [Requires Pro+ plan] [DEPRECATED — scheduled for removal] Get cached failed run history for a flow from the Power Clarity store (convenience wrapper around get_store_flow_runs with status=Failed). Returns failedActions and remediation hint per run to help diagnose issues. Data is from the stored snapshot — not live from the Power Automate API. Use get_live_flow_runs and filter by status=Failed instead.
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  • axint.run.status

    Axint

    Read the latest or selected Axint run job record, including active child process IDs. Use this when a long xcodebuild run may still be active after an MCP timeout or client disconnect. Use: use after MCP timeouts or long builds to resume without guessing whether xcodebuild is still active. Effects: read-only local run/job inspection; writes no files and uses no network.
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  • run_audit

    mcp

    Run a full AEO audit of a URL covering FIND, READ, and USE. Async: returns an audit_id to poll with get_audit. Accepts an optional target_keyword. Spends one audit credit per fresh run; a same-URL re-run within 24h reuses the cached audit, uncharged.
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  • get_last_worker_run_log

    mcp-coreclaw

    Get logs for the current user's most recent CoreClaw worker run. WHEN TO USE: Use when debugging why the latest run failed, stalled, or produced unexpected output. 中文触发: 当用户要在 CoreClaw 中查询、运行、重跑、停止、导出或查看对应 worker/run/task 数据时使用。 WHEN NOT TO USE: Do not use public web search or code search for private CoreClaw platform data. Do not call excluded internal worker-version or internal-detail APIs. RETURNS: JSON with recent run log data. WORKFLOW: Call after get_last_worker_run, especially for failed or running states.
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  • Bullrun

    Global stock research, ML forecasts, valuation signals, screeners & portfolio tracking in Claude

  • Google Cloud Run

    Deploy applications to Cloud Run

  • me.validation_history

    AI Design Blueprint

    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 <token>. Pro or Teams plan required.
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  • start_simulation

    Hydrata - ANUGA Flood Simulation

    Start a flood simulation run for a built scenario. The scenario must be in 'built' status. Returns 202 with the new run. The run transitions through: built → queued → computing → processing → complete. After starting, poll get_run_status to track progress. Returns 409 if the scenario is not in the correct state.
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  • get_run

    Hydrata - ANUGA Flood Simulation

    Get full details of a simulation run including timing and results. Returns the complete run record: status, progress, timing (start/end timestamps, duration), compute details (backend, instance type, cost), mesh info, error messages, and result log. Use get_run_status for lightweight polling; use this for final results.
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  • deploy_service_from_file_contents

    Google Cloud Run

    Deploys a Cloud Run service directly from local source files. This method is suitable for scripting languages like Python and Node.js, of which the source code can be embedded in the request. This is ideal for quick tests and development feedback loops. You must include all necessary dependencies within the source files because it skips the build step for faster deployment. **Key Requirements:** 1. source_code: Should set to sourceCode.inlinedSource.sources with array of source files, each having `filename` and `content`. 2. Size limit: you are subject to total request size limit of 50MiB.
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  • fx_dataframe_query

    exchange-rates-mcp-server

    Run a read-only SQL SELECT against DataCanvas tables staged by fx_get_timeseries. Supports aggregations, GROUP BY, window functions, and JOINs across multiple registered tables. Run fx_dataframe_describe first to discover table names and column schemas.
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  • regenerate_forecast_narrative

    clariBI.com

    Re-run ONLY the AI narration step against an existing completed forecast run. Costs 1-2 AI credits (no engine work). Returns the new narration; the old one is overwritten in the run record. Refuses when the run is not yet complete or when narration_enabled=false on the forecast.
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  • analyze_flat

    flatscope

    Start a Flatscope research run on a UK Rightmove or Zoopla listing. Returns an analysis id and an access token immediately; the run takes a few minutes, so then poll get_flat_analysis with the id and token. England and Wales only, Rightmove and Zoopla. The first few runs per user are free.
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  • overview

    delegation-school

    What delegation.school is, the three tracks, and how to run the course as the learner's tutor. Background reading.
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  • scaffold_local

    mcp

    OPTIONAL preflight: returns a podman-compose.yml + .env so the user can run the app (and a throwaway local Postgres) on THEIR machine before deploying to redu — to see it run / sanity-check the container. Requires local podman/podman-compose. It's a suggestion, not a gate — skip it and go straight to deploy_app any time. Honest caveat: the local Postgres is NOT the managed Postgres, so a green local run does not prove the prod DB wiring.
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  • list_scans

    email-deliverability

    List recent scans (id, domain, run time, score) across all tracked domains, or for one domain when a name is given.
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  • get_results

    operant-mcp

    Return all model calibration profiles (OCS mean/stdev, orchestration mean, run_family, subject_shell), plus generated_at, included_lab_labels, and the not-a-flat-leaderboard caveat. Models are returned as-is — do not pre-sort into a naive leaderboard. Single-run models (null stdev) must not be ranked as-if reliable against multi-run models.
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