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Model Context Protocol (MCP) server for interacting with fal.ai models and services. Provides tools for model discovery, content generation, and file uploads.
The server provides 12 well-structured tools with generally comprehensive schemas and descriptions. Strengths: all tools have clear descriptions (mostly 50-200 chars), input schemas are well-defined with proper JSON Schema structure, and parameter descriptions are generally present and specific. Most parameters include type constraints and enums where appropriate. Weaknesses: output schemas are NOT documented (critical gap for agent planning); descriptions focus on functionality but lack dependency hints and recovery guidance; error handling and validation are not visible in the tool definitions; no tool annotations (readOnlyHint/destructiveHint/idempotentHint); parameter relationships (e.g., endpoint_ids array bounds 1-50) are documented in descriptions rather than formal constraints. The tools are read-heavy (10/12 read-only), covering model discovery, generation tracking, and billing analysis, a good domain fit for fal.ai, but lack error recovery guidance.
Get detailed analytics data for workspace including model usage statistics, performance metrics, and trends. Requires authentication.
Cancel a pending or processing generation request.
Estimate costs for model operations using historical API pricing or unit pricing. Requires authentication. Useful for budget planning and cost optimization.
Find specific model(s) by endpoint ID. Can retrieve single or multiple models (1-50). Useful for looking up exact models by their stable identifiers.
Submit a generation request to a fal.ai model. This queues the request and returns immediately with a request_id for tracking.
List available models in the fal.ai model gallery. Supports optional filtering by category, status, and cursor-based pagination. Can expand OpenAPI schemas inline with expand parameter.
Output schemas are NOT documented. Tools return results but LLMs have no visibility into what fields to expect, preventing effective downstream composition and forcing agents to discover fields by trial-and-error.
Tool annotations (readOnlyHint, destructiveHint, idempotentHint) are missing. This prevents clients from understanding which tools are safe to retry, which modify state, and which are read-only, critical for robust agent planning.
Descriptions for 'result' and 'status' tools are minimal (55-60 chars). They lack context on when to call each, dependency hints (e.g., 'Call generate() first'), and what the status field values mean (pending, completed, failed, etc.).
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 59 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 42 | - | v1 |
Get pricing information for specific model endpoint(s). Returns unit pricing with currency. Requires authentication. Most models use output-based pricing (per image/video).
Get the result of a generation request.
Search for models in the fal.ai gallery using free-text query across name, description, and category. Supports filtering and cursor-based pagination.
Check the status of a generation request without fetching full results.
Upload a file to fal.ai CDN for use with models. Returns a URL that can be used as input to generation requests.
Get usage records for workspace with detailed billing information. Returns time series data and/or summary statistics with unit quantities and prices. Requires authentication.
Parameter 'input_data' in generate() tool is typed as object with only generic description 'Dictionary of input parameters for the model (model-specific)'. No guidance on what fields are required per model, no link to model schema discovery (via 'find' tool with expand='openapi-3.0'), and no validation rules.
Error handling is not visible in tool definitions. No recovery guides (e.g., 'if status=failed, try a different model'), no retryability classification, and no actionable error messages visible. Agents lack guidance on how to recover from common failures.
Parameter bounds documented only in description text (e.g., 'endpoint_ids... 1-50 models', 'limit... max: 100'). Should be enforced via JSON Schema minItems/maxItems and maxLength constraints, which are both machine-parseable and prevent LLM confusion.
Descriptions for billing tools (pricing, estimate_cost, usage, analytics) mention 'Requires authentication' but do not explain authentication mechanism (API key, OAuth), whether it's implicit (server-side), or user-provided. This creates ambiguity for deployed agents.