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Flowise
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Flowise

mcp-flowise is a Python package that implements a Model Context Protocol (MCP) server integrated with the Flowise API, supporting dynamic tool registration and two operation modes.
Artificial intelligence chatbotsDeveloper tools#Flowise Integration#Dynamic Tools#MCP Protocol#Chat Flows.Python
2 points
13

What is MCP Flowise?

MCP Flowise is a server based on the Model Context Protocol (MCP) that can integrate the Flowise API and dynamically create chat flow tools. It offers two operation modes: Low-level mode (default) and Fast MCP mode.

How to use MCP Flowise?

By setting environment variables and configuration files, you can easily start the server and begin using it. It supports dynamic tool registration and static configuration.

Applicable Scenarios

Suitable for developers and enterprise users who need to dynamically integrate chat flow tools, especially in scenarios where they want to use the Flowise API within the MCP ecosystem.

Main Features

Dynamic Tool RegistrationAutomatically creates independent tools for each chat flow in low-level mode.
Fast MCP ModeSimplifies configuration by only exposing two core tools: List chat flows and Generate predictions.
Filtering SupportSupports whitelisting and blacklisting chat flows through ID or name regular expressions.

Advantages and Limitations

Advantages
Supports dynamic tool registration, reducing manual configuration workload.
Two operation modes adapt to different complexity requirements.
A flexible filtering mechanism ensures that only relevant chat flows are displayed.
Seamlessly integrates into the MCP ecosystem.
Limitations
Requires Python 3.12 or higher.
Additional path configuration may be required in a Windows environment.
Familiarity with environment variable management may be needed for advanced features.

How to Use

Install Dependencies
Ensure that Python 3.12 or higher is installed and the uvx package manager is configured.
Clone the Repository
Clone the mcp-flowise repository from GitHub to your local machine.
Configure Environment Variables
Set the necessary environment variables such as FLOWISE_API_KEY and FLOWISE_API_ENDPOINT.
Start the Server
Run the server to load chat flows and tools.

Usage Examples

List All Chat FlowsDemonstrate how to obtain a list of available chat flows through the API.
Generate PredictionsDemonstrate how to generate prediction results for a specific chat flow.

Frequently Asked Questions

How to switch to Fast MCP mode?
Why can't my chat flows be loaded?
Does it support custom chat flow descriptions?

Related Resources

GitHub Repository
Official code repository and documentation.
Smithery Official Website
A tool for automated installation and configuration.
MCP Protocol Specification
Understand the core concepts of the Model Context Protocol.
Installation
Copy the following command to your Client for configuration
{
    "mcpServers": {
        "mcp-flowise": {
            "command": "uvx",
            "args": [
                "--from",
                "git+https://github.com/andydukes/mcp-flowise",
                "mcp-flowise"
            ],
            "env": {
                "FLOWISE_API_KEY": "${FLOWISE_API_KEY}",
                "FLOWISE_API_ENDPOINT": "${FLOWISE_API_ENDPOINT}"
            }
        }
    }
}

{
  "mcpServers": {
    "flowise": {
      "command": "C:\\Users\\matth\\.local\\bin\\uvx.exe",
      "args": [
        "--from",
        "C:\\Users\\matth\\downloads\\mcp-flowise",
        "mcp-flowise"
      ],
      "env": {
        "LOGLEVEL": "ERROR",
        "APPDATA": "C:\\Users\\matth\\AppData\\Roaming",
        "FLOWISE_API_KEY": "your-api-key-goes-here",
        "FLOWISE_API_ENDPOINT": "http://localhost:3010/"
      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.
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