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fal.ai MCP Server

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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.1/5.0

    Scored across 66 tools

    Disambiguation2/5

    run_model, submit_job, generate_image, and generate_video share byte-identical descriptions ('Confirmed paid model request after current native input-schema validation...'), so an agent cannot tell which to pick. There is also overlap among estimate_pricing/get_pricing and get_usage/get_billing_events/get_account_billing, though most asset, storage, and workflow tools are distinct.

    Naming Consistency5/5

    Nearly every tool follows a clean verb_noun snake_case pattern (get_asset, create_asset_collection, delete_asset_tag, list_asset_characters, set_storage_file_acl). Even the generation and job tools (run_model, submit_job, cancel_job, preview_generation_batch) fit the same convention, with no camelCase or mixed styles.

    Tool Count1/5

    66 tools far exceeds a well-scoped surface and crosses the extreme-mismatch threshold. The count is inflated by near-duplicate generation tools (run_model/submit_job/generate_image/generate_video) and a sprawling asset API, making the set unwieldy for an agent to navigate.

    Completeness4/5

    Coverage is broad and genuinely deep: full CRUD for asset collections, characters, and tags; billing, usage, analytics, storage ACL/lifecycle, jobs, requests, and organization endpoints. The main gap is workflows, which support list/create/get but no update or delete, leaving a lifecycle dead end.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources