fal.ai MCP Server
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TDQS
Scored across 66 tools
Several tools have overlapping or identical purposes: run_model, submit_job, generate_image, and generate_video all share the exact same description, making it impossible to distinguish when to use which. search_requests and list_requests_by_endpoint also overlap heavily, and list_assets vs list_asset_collection_assets vs get_asset cover similar ground.
Most tools follow a consistent verb_noun snake_case pattern (get_model_info, create_workflow, delete_asset_collection). A few deviations like assign_asset_tag/unassign_asset_tag vs set_asset_tags_for_asset add minor inconsistency but the overall scheme is predictable.
66 tools is far too many for coherent agent use; the set is bloated with many near-duplicate CRUD operations for assets, collections, characters, and tags. This volume forces significant disambiguation burden on the caller.
The surface covers many domains (models, pricing, usage, analytics, billing, workflows, assets, storage, jobs) with reasonable CRUD completeness for assets and collections. However, clear gaps exist: no list_workflows deletion/update, no search_assets tool despite list_assets mentioning semantic search, and no clear way to poll/cancel batch jobs beyond the single cancel_job.