Skip to main content
Glama
650,527 tools. Updated 2026-10-14 04:26

"Astro" matching MCP tools:

  • Search Astro documentation to find relevant information for Astro-related tasks. This tool enables AI assistants to retrieve specific content from the Astro docs for accurate user support.
    -
  • Trigger an Apache Airflow DAG run and wait for its completion, returning the final state and details of any failed tasks. Blocks until the run ends or times out (default 60 minutes).
    Apache 2.0
  • Retrieve all configured resource pools with slot counts, occupancy, and capacity to manage task concurrency and resource allocation.
    Apache 2.0

Matching MCP Servers

  • A
    license
    A
    quality
    B
    maintenance
    Python/stdio MCP connector providing timestamped crypto intelligence for ten assets: prediction-market intelligence, market regimes, sentiment, funding, liquidity, asset rankings, and component-score explanations. Public access without signup, API keys, or trial activation; no trade execution.
    4
    2
    MIT
  • F
    license
    Not graded
    quality
    B
    maintenance
    Enables Claude and other MCP clients to compute Vedic astrology chart facts locally with a private Swiss Ephemeris engine — positions, divisional charts, dashas, transits, Shadbala, Ashtakavarga, Jaimini analysis, birth-time sensitivity, and PDF-ready structured reports. All calculation runs on the user's own machine, keeping birth data local while the interpretation layer reasons over validated chart facts rather than inventing them.
    -

Matching MCP Connectors

  • Push updated pages and assets to an existing hosted website. Set sync=true for full site redeploys to delete stale files; use sync=false to edit selected pages.
    MIT
  • Retrieve detailed information about a specific task instance in Apache Airflow, including its state, start/end times, duration, retry attempts, operator type, and pool assignment. Use this to investigate failures or monitor task execution.
    Apache 2.0
  • List asset events produced by task updates. Filter by source DAG, run, or task. Use to debug downstream DAG triggers and track which assets a pipeline produced.
    Apache 2.0
  • Troubleshoot failed or problematic Airflow DAG runs by retrieving run metadata, task instance states, and highlighting failed tasks for quick diagnosis.
    Apache 2.0
  • Generate Markdown files for technical documentation in code repositories. Supports auto table of contents, YAML frontmatter, and full markdown syntax for GitHub-ready content.
    MIT
  • Pause a DAG to prevent new scheduled runs while allowing currently running tasks to complete. Use when you need to stop scheduled execution without terminating active tasks.
    Apache 2.0
  • Retrieve detailed information about a specific Airflow DAG run, including its state, start and end times, duration, run type, configuration, and execution metadata.
    Apache 2.0
  • Resume scheduled runs for a paused DAG by unpausing it, enabling the scheduler to create new runs based on the schedule interval.
    Apache 2.0
  • List all configured Airflow connections to external systems (databases, APIs, services) and retrieve their metadata such as connection ID, type, host, port, and schema. Passwords are never returned for security.
    Apache 2.0
  • Retrieve an Airflow variable by key to access its value and metadata, enabling DAG configuration and sharing settings across workflows.
    Apache 2.0
  • Retrieve comprehensive details of any Airflow DAG in one call, including metadata, tasks, dependencies, source code, and errors. Ideal for understanding DAG structure and diagnosing issues without multiple API requests.
    Apache 2.0
  • Retrieve logs for a specific task execution. View stdout, stderr, error messages, and timing info to debug failures. Specify DAG, run, task, try number, and map index.
    Apache 2.0
  • List all DAG runs across Airflow with execution status, dates, and run type. Monitor workflow history and quickly identify failed or successful runs.
    Apache 2.0
  • Retrieve all tracked data assets and datasets in Airflow, including their URIs, IDs, creation and update times, and relationships with consuming DAGs and producing tasks. Ideal for inspecting data lineage and dependencies.
    Apache 2.0