mcp-flowise

mcp-flowise

The Flowise MCP Server enables clients to list chatflows and call predictions, integrating seamlessly with DIY Flowise or Flowise Cloud accounts. It provides a simple interface for executing chatflows/assistants predictions with existing Flowise configurations.

Category
Visit Server

README

mcp-flowise

smithery badge

mcp-flowise is a Python package implementing a Model Context Protocol (MCP) server that integrates with the Flowise API. It provides a standardized and flexible way to list chatflows, create predictions, and dynamically register tools for Flowise chatflows or assistants.

It supports two operation modes:

  • LowLevel Mode (Default): Dynamically registers tools for all chatflows retrieved from the Flowise API.
  • FastMCP Mode: Provides static tools for listing chatflows and creating predictions, suitable for simpler configurations.

<p align="center"> <img src="https://github.com/user-attachments/assets/d27afb05-c5d3-4cc9-9918-f7be8c715304" alt="Claude Desktop Screenshot"> </p>


Features

  • Dynamic Tool Exposure: LowLevel mode dynamically creates tools for each chatflow or assistant.
  • Simpler Configuration: FastMCP mode exposes list_chatflows and create_prediction tools for minimal setup.
  • Flexible Filtering: Both modes support filtering chatflows via whitelists and blacklists by IDs or names (regex).
  • MCP Integration: Integrates seamlessly into MCP workflows.

Installation

Installing via Smithery

To install mcp-flowise for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @matthewhand/mcp-flowise --client claude

Prerequisites

  • Python 3.12 or higher
  • uvx package manager

Install and Run via uvx

Confirm you can run the server directly from the GitHub repository using uvx:

uvx --from git+https://github.com/matthewhand/mcp-flowise mcp-flowise

Adding to MCP Ecosystem (mcpServers Configuration)

You can integrate mcp-flowise into your MCP ecosystem by adding it to the mcpServers configuration. Example:

{
    "mcpServers": {
        "mcp-flowise": {
            "command": "uvx",
            "args": [
                "--from",
                "git+https://github.com/matthewhand/mcp-flowise",
                "mcp-flowise"
            ],
            "env": {
                "FLOWISE_API_KEY": "${FLOWISE_API_KEY}",
                "FLOWISE_API_ENDPOINT": "${FLOWISE_API_ENDPOINT}"
            }
        }
    }
}

Modes of Operation

1. FastMCP Mode (Simple Mode)

Enabled by setting FLOWISE_SIMPLE_MODE=true. This mode:

  • Exposes two tools: list_chatflows and create_prediction.
  • Allows static configuration using FLOWISE_CHATFLOW_ID or FLOWISE_ASSISTANT_ID.
  • Lists all available chatflows via list_chatflows.

<p align="center"> <img src="https://github.com/user-attachments/assets/0901ef9c-5d56-4f1e-a799-1e5d8e8343bd" alt="FastMCP Mode"> </p>

2. LowLevel Mode (FLOWISE_SIMPLE_MODE=False)

Features:

  • Dynamically registers all chatflows as separate tools.
  • Tools are named after chatflow names (normalized).
  • Uses descriptions from the FLOWISE_CHATFLOW_DESCRIPTIONS variable, falling back to chatflow names if no description is provided.

Example:

  • my_tool(question: str) -> str dynamically created for a chatflow.

Running on Windows with uvx

If you're using uvx on Windows and encounter issues with --from git+https, the recommended solution is to clone the repository locally and configure the mcpServers with the full path to uvx.exe and the cloned repository. Additionally, include APPDATA, LOGLEVEL, and other environment variables as required.

Example Configuration for MCP Ecosystem (mcpServers on Windows)

{
  "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:3000/"
      }
    }
  }
}

Notes

  • Full Paths: Use full paths for both uvx.exe and the cloned repository.
  • Environment Variables: Point APPDATA to your Windows user profile (e.g., C:\\Users\\<username>\\AppData\\Roaming) if needed.
  • Log Level: Adjust LOGLEVEL as needed (ERROR, INFO, DEBUG, etc.).

Environment Variables

General

  • FLOWISE_API_KEY: Your Flowise API Bearer token (required).
  • FLOWISE_API_ENDPOINT: Base URL for Flowise (default: http://localhost:3000).

LowLevel Mode (Default)

  • FLOWISE_CHATFLOW_DESCRIPTIONS: Comma-separated list of chatflow_id:description pairs. Example:
    FLOWISE_CHATFLOW_DESCRIPTIONS="abc123:Chatflow One,xyz789:Chatflow Two"
    

FastMCP Mode (FLOWISE_SIMPLE_MODE=true)

  • FLOWISE_CHATFLOW_ID: Single Chatflow ID (optional).
  • FLOWISE_ASSISTANT_ID: Single Assistant ID (optional).
  • FLOWISE_CHATFLOW_DESCRIPTION: Optional description for the single tool exposed.

Filtering Chatflows

Filters can be applied in both modes using the following environment variables:

  • Whitelist by ID:
    FLOWISE_WHITELIST_ID="id1,id2,id3"
  • Blacklist by ID:
    FLOWISE_BLACKLIST_ID="id4,id5"
  • Whitelist by Name (Regex):
    FLOWISE_WHITELIST_NAME_REGEX=".*important.*"
  • Blacklist by Name (Regex):
    FLOWISE_BLACKLIST_NAME_REGEX=".*deprecated.*"

Note: Whitelists take precedence over blacklists. If both are set, the most restrictive rule is applied.

Security

  • Protect Your API Key: Ensure the FLOWISE_API_KEY is kept secure and not exposed in logs or repositories.
  • Environment Configuration: Use .env files or environment variables for sensitive configurations.

Add .env to your .gitignore:

# .gitignore
.env

Troubleshooting

  • Missing API Key: Ensure FLOWISE_API_KEY is set correctly.
  • Invalid Configuration: If both FLOWISE_CHATFLOW_ID and FLOWISE_ASSISTANT_ID are set, the server will refuse to start.
  • Connection Errors: Verify FLOWISE_API_ENDPOINT is reachable.

License

This project is licensed under the MIT License. See the LICENSE file for details.

TODO

  • [x] Fastmcp mode
  • [x] Lowlevel mode
  • [x] Filtering
  • [x] Claude desktop integration
  • [ ] Assistants

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured