Loading ToolBench
MCP server providing access to Fal.ai models with comprehensive discovery, filtering, and integration capabilities for image, video, audio, and text generation models
Server exhibits solid definition quality with well-structured tools, comprehensive parameter documentation, and thoughtful descriptions. All 4 tools have explicit schemas, meaningful descriptions, and clear use-case guidance. However, output schemas are not formally documented in code, and error handling lacks recovery guidance. Naming is strong and follows verb-noun convention. Parameter descriptions are detailed and include constraints. The implementation shows maturity in schema design using Zod and includes tool annotations (readOnlyHint).
Get comprehensive documentation for a Fal.ai model including usage examples, parameter descriptions, and best practices.
Get detailed information about a specific Fal.ai model including metadata, capabilities, description, and pricing. Use this tool when: - User asks about a specific model - User wants to know what a model does - User needs model metadata - User asks about model pricing or cost - Coding agent needs model details for integration Returns comprehensive model information including ID, name, description, category, owner, capabilities, and pricing (when API key is provided).
Get the OpenAPI/JSON schema for a Fal.ai model's input and output parameters. Use this tool when: - User needs to know what parameters a model accepts - User asks about model API structure - User wants to integrate a model programmatically - Coding agent needs to understand model interface - Developer needs request/response schema Returns the complete schema including required/optional parameters, types, examples, and validation rules.
List all available Fal.ai models for image, video, and audio generation with comprehensive filtering and sorting options. Use this tool when: - User asks about available models - User wants to discover models by category, status, tags, or owner - User needs to find specific model types (e.g., "show me new image models") - User asks "what models are available?" or "show me image models" - Coding agent needs to discover what models exist - User wants to filter by tags like "new", "beta", "pro", "turbo" - User wants to find models from a specific owner/organization Filtering Options: - category: Filter by API category from metadata.category (e.g., "text-to-image", "image-to-video") or array of categories (models must match ANY specified category) Common API categories: "text-to-image", "image-to-image", "text-to-video", "image-to-video", "video-to-video", "image-to-3d", "text-to-audio", "audio", "audio-to-audio", "speech-to-text", "text-to-speech", "training" - status: Filter by status (active, deprecated) - tags: Filter by tags - models must have ALL specified tags (e.g., ["new", "beta"]) - owner: Filter by model owner/organization (e.g., "fal-ai", "clarityai") - highlighted: Filter to only highlighted models - pinned: Filter to only pinned models - kind: Filter by model kind (inference, training) - search: Free-text search across name, description, ID, and tags Sorting Options: - updated_at: Most recently updated models first (default for discovery) - name: Alphabetical by name - category: Grouped by category, then alphabetical - owner: Grouped by owner, then alphabetical Returns a formatted list of models with IDs, names, descriptions, and categories.
Output schemas not formally documented. While input schemas are well-defined via Zod, the response structures are not declared, LLMs cannot know what fields to expect from tool responses without parsing markdown text.
Error responses lack recovery guidance. When a model is not found or an error occurs, the tool returns generic error text but does not guide the LLM on next steps (e.g., 'Use list_models to discover available models' is mentioned in get_model_info but not consistently applied across all tools).
API key extraction relies on RequestHandlerExtra context which may be fragile. The getApiKeyFromExtra function attempts multiple property paths but does not validate that the header was actually found, missing Authorization header silently returns undefined, potentially leading to downgraded API calls without user awareness.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | C | 68 | 2026-07-28+ | v2 |
| 2026-03-09 | D | 57 | - | v1 |
No pagination support visible in list_models response. Tool accepts a 'limit' parameter (default 20, max 100) but the response does not include pagination metadata (total count, next_cursor, or offset info) needed for agents to iterate through large result sets.
get_model_schema and get_model_documentation lack sufficient context in descriptions. Descriptions state WHAT they return but not WHEN an agent should choose between them or how they differ from get_model_info.