Converts Figma frames into front-end code for various mobile frameworks.

mcp-flowise
3 years
Works with Finder
1
Github Watches
7
Github Forks
23
Github Stars
mcp-flowise
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.
Features
- Dynamic Tool Exposure: LowLevel mode dynamically creates tools for each chatflow or assistant.
-
Simpler Configuration: FastMCP mode exposes
list_chatflows
andcreate_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
andcreate_prediction
. - Allows static configuration using
FLOWISE_CHATFLOW_ID
orFLOWISE_ASSISTANT_ID
. - Lists all available chatflows via
list_chatflows
.
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 ofchatflow_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
andFLOWISE_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
- Fastmcp mode
- Lowlevel mode
- Filtering
- Claude desktop integration
- Assistants
相关推荐
I find academic articles and books for research and literature reviews.
Confidential guide on numerology and astrology, based of GG33 Public information
Embark on a thrilling diplomatic quest across a galaxy on the brink of war. Navigate complex politics and alien cultures to forge peace and avert catastrophe in this immersive interstellar adventure.
Advanced software engineer GPT that excels through nailing the basics.
Delivers concise Python code and interprets non-English comments
💬 MaxKB is an open-source AI assistant for enterprise. It seamlessly integrates RAG pipelines, supports robust workflows, and provides MCP tool-use capabilities.
The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, MCP compatibility, and more.
Micropython I2C-based manipulation of the MCP series GPIO expander, derived from Adafruit_MCP230xx
MCP server to provide Figma layout information to AI coding agents like Cursor
AI Agents & MCPs & AI Workflow Automation • (280+ MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
Reviews

user_tTjSslMy
As a dedicated user of mcp, I must say that mcp-flowise by matthewhand is a game-changer! The seamless integration and intuitive design make workflow management a breeze. The attention to detail and the user-friendly interface have significantly improved my productivity. Highly recommended for anyone looking to optimize their workflow! Check it out at https://github.com/matthewhand/mcp-flowise.