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derekalia

fal

by derekalia
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
PORTNoServer port for HTTP transport6274
FAL_KEYYesYour fal.ai API key
MCP_TRANSPORTNoTransport mode - stdio (default) or httpstdio

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
modelsA
    List available models on fal.ai. Ensure to use the total and page arguments. Avoid listing all the models at once.
    
    Args:
        page: The page number of models to retrieve (pagination)
        total: The total number of models to retrieve per page
        
    Returns:
        A list of models with their metadata
searchC
    Search for models on fal.ai based on keywords.
    
    Args:
        keywords: The search terms to find models
        
    Returns:
        A list of models matching the search criteria
schemaB
    Get the OpenAPI schema for a specific model.
    
    Args:
        model_id: The ID of the model (e.g., "fal-ai/flux/dev")
        
    Returns:
        The OpenAPI schema for the model
generateC
    Generate content using a fal.ai model.
    
    Args:
        model: The model ID to use (e.g., "fal-ai/flux/dev")
        parameters: Model-specific parameters as a dictionary
        queue: Whether to use the queuing system (default: False)
        
    Returns:
        The model's response
resultB
    Get the result of a queued request.
    
    Args:
        url: The response_url from a queued request
        
    Returns:
        The generation result
statusA
    Check the status of a queued request.
    
    Args:
        url: The status_url from a queued request
        
    Returns:
        The current status of the queued request
cancelB
    Cancel a queued request.
    
    Args:
        url: The cancel_url from a queued request
        
    Returns:
        The result of the cancellation attempt
uploadC
    Upload a file to fal.ai storage.
    
    Args:
        path: The absolute path to the file to upload
        
    Returns:
        Information about the uploaded file, including the file_url

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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

A3.5/5.0

Scored across 8 tools

Disambiguation4/5

Most tools have distinct purposes, but there is some overlap between 'models' and 'search' as both retrieve model information, which could cause confusion. However, their specific functions (listing vs. keyword-based searching) are clarified in descriptions, preventing major misselection.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with clear, single-word verbs (e.g., cancel, generate, models, result). This uniformity makes the tool set predictable and easy to navigate, with no deviations in style.

Tool Count5/5

With 8 tools, the server is well-scoped for interacting with fal.ai's API, covering key operations like content generation, model management, and file handling. Each tool serves a specific function without unnecessary bloat.

Completeness4/5

The tool set covers core workflows for AI model interaction, including generation, queuing, model discovery, and file uploads. A minor gap exists in lacking tools for direct model management (e.g., updating or deleting models), but agents can work effectively with the provided operations.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources