A CLI tool for installing and managing Model Context Protocol (MCP) servers
Project description
FluidMCP CLI
A powerful command-line interface for managing and running MCP (Model Context Protocol) servers. FluidMCP CLI allows you to install, configure, and run MCP packages from the FluidMCP registry with ease.
Features
- 🚀 Install MCP packages from the FluidMCP registry
- 🔧 Interactive environment variable configuration
- 🌐 Run individual or multiple MCP servers
- 🔒 Secure mode with bearer token authentication
- ☁️ S3-based configuration management
- 📁 Local and remote configuration file support
- 🎯 Automatic port management and conflict resolution
Installation
pip install fluidmcp
Quick Start
Install a package:
fluidmcp install author/package@version
List installed packages:
fluidmcp list
Run a package:
fluidmcp run package --port --start-server
Commands
install
Install MCP packages from the FluidMCP registry.
fluidmcp install <author/package@version> [--master]
Examples:
# Install a specific version
fluidmcp install Perplexity/perplexity-ask@1.0.0
# Install latest version
fluidmcp install Perplexity/perplexity-ask
# Install with master env file (skips API key prompts)
fluidmcp install Perplexity/perplexity-ask --master
Options:
--master: Use master environment file for API keys (skips interactive prompts)
Package Format:
author/package@version- Install specific versionauthor/package- Install latest version
run
Run MCP servers from installed packages or configuration files.
fluidmcp run <package|all|file> [options]
Examples:
# Run a specific package with FastAPI client server
fluidmcp run Airbnb/airbnb@0.1.0 --port 8200 --start-server
# Run all installed packages
fluidmcp run all
# Run with master S3 configuration
fluidmcp run all --master
# Run from local configuration file
fluidmcp run <path-to-your-directory>config.json --file
# Run from S3 presigned URL
fluidmcp run https://s3.amazonaws.com/bucket/config.json --s3
# Run with secure mode and custom token
fluidmcp run Fluid_Ai/jupyter@1.0.0 --secure your-bearer-token --port 8200 --start-server
Options:
--port <number>: Specify port for SuperGateway (default: 8111)--start-server: Start FastAPI client server--force-reload: Force reload by killing existing processes without prompt--master: Use master metadata file from S3--secure: Enable secure mode with bearer token authentication--token <token>: Bearer token for secure mode (auto-generated if not provided)--file: Treat package argument as path to local JSON configuration file--s3: Treat package argument as S3 presigned URL to configuration file
list
Display all installed MCP packages.
fluidmcp list
Output format:
Installation directory: /path/to/.fmcp-packages
author1/package1@1.0.0
author2/package2@2.1.0
edit-env
Interactively edit environment variables for installed packages.
fluidmcp edit-env <authorname/package@version>
Example:
# Edit environment variables for a specific package
fluidmcp edit-env Fluid_Ai/jupyter@1.0.0
Features:
- Interactive environment variable editor
- Secure masking of API keys and sensitive data
- Support for both structured and simple environment formats
- Real-time validation and updates
Configuration
Environment Variables
FluidMCP CLI uses several environment variables for configuration:
# S3 Configuration (for --master mode)
export S3_BUCKET_NAME="your-bucket"
export S3_ACCESS_KEY="your-access-key"
export S3_SECRET_KEY="your-secret-key"
export S3_REGION="us-east-1"
# Registry configuration
export MCP_FETCH_URL="https://registry.fluidmcp.com/fetch-mcp-package"
export MCP_TOKEN="your-registry-token"
Package Structure
Installed packages follow this directory structure:
.fmcp-packages/
├── author1/
│ └── package1/
│ └── 1.0.0/
│ ├── metadata.json
│ └── [package files]
└── author2/
└── package2/
└── 2.1.0/
├── metadata.json
└── [package files]
Metadata Format
Each package contains a metadata.json file:
{
"mcpServers": {
"package-name": {
"command": "npx",
"args": ["-y", "@package/server"],
"env": {
"API_KEY": "value"
}
}
}
}
S3 Files
For S3 operations, you can use following Json format:
{
"mcpServers": {
"server1": {
"command": "npx",
"args": ["-y", "example-package@1.0.0"],
"env": {
"API_KEY": "your-api-key"
},
"port": "8100",
"install_path": "/path/to/.fmcp-packages/Author/package/1.0.0",
"fmcp_package": "Author/package@1.0.0"
},
"server2": {
"command": "uvx",
"args": ["another-server"],
"env": {
"TOKEN": "value"
},
"port": "8101",
"install_path": "/path/to/.fmcp-packages/Author/another-package/2.0.0",
"fmcp_package": "Author/another-package@2.0.0"
}
}
}
Trying out an MCP server
Create MCP Server:
pip install fluidmcp
fmcp install "Google Maps MCP Server/google-maps@0.6.2"
Output:
:wrench: Installing Google Maps MCP Server/google-maps@0.6.2
:cloud: Installing package from Fluid MCP registry...
:cloud: Downloading packages
:package: Saving metadata.json...
:information_source: Reading metadata.json for API key configuration
:key: Saving API key(s) to metadata.json
:information_source: API key(s) saved to metadata.json.
:white_check_mark: Installation completed successfully.
fmcp list
Output:
Installation directory: /content/.fmcp-packages
Google Maps MCP Server/google-maps@0.6.2
fmcp run all
Output:
✅ Wrote merged metadata to /content/.fmcp-packages/metadata_all.json
:blue_book: Reading metadata.json from /content/.fmcp-packages/Google Maps MCP Server/google-maps/0.6.2/metadata.json
google-maps {'command': 'npx', 'args': ['-y', '@modelcontextprotocol/server-google-maps'], 'env': {'GOOGLE_MAPS_API_KEY': '<YOUR_API_KEY>'}}
✅ Added google-maps endpoints
2025-06-06 09:19:04.284 | INFO | fluidai_mcp.cli:run_all:279 - Starting FastAPI client server on port 8099
INFO: Started server process [6848]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8099 (Press CTRL+C to quit)
Quick-trying out MCP server:
# IMP NOTE: You have to add GOOGLE_MAPS_API_KEY in .fmcp-packages/{Package}/metadata.json
import requests
import json
mcp_url = "http://localhost:8099/google-maps/mcp"
mcp_payload = {
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "maps_search_places",
"arguments": {
"query": "coffee shops in San Francisco"
}
}
}
mcp_response = requests.post(mcp_url, json=mcp_payload)
print("Status code:", mcp_response.status_code)
# Print the response in pretty JSON format
try:
parsed_response = json.loads(mcp_response.text)
for item in parsed_response["result"]["content"]:
if item["type"] == "text":
nested_json = json.loads(item["text"])
print(json.dumps(nested_json, indent=2))
# print(json.dumps(mcp_response.json(), indent=2))
except Exception as e:
print("Error decoding JSON:", e)
Output:
Status code: 200
{
"places": [
{
"name": "The Coffee Movement",
"formatted_address": "1030 Washington St, San Francisco, CA 94108, United States",
"location": {
"lat": 37.7947575,
"lng": -122.4102936
},
"place_id": "ChIJoTMb3PKAhYARsXkQt469sBw",
"rating": 4.8,
"types": [
"cafe",
"food",
"point_of_interest",
"store",
"establishment"
]
},
{
"name": "Sextant Coffee Roasters",
"formatted_address": "1415 Folsom St, San Francisco, CA 94103, United States",
"location": {
"lat": 37.7724716,
"lng": -122.4040877
},
"place_id": "ChIJW9Zu0uGAhYARQ5p0JqvV0Ew",
"rating": 4.6,
"types": [
"cafe",
"restaurant",
"food",
"point_of_interest",
"store",
"establishment"
]
}
]
}
You can further checkout 'examples/chat_google_maps.ipynb'
Using Server-Sent Events (SSE) for Streaming Responses
For MCP tools that support incremental or chunked data delivery, FluidMCP provides a Server-Sent Events (SSE) endpoint. This is particularly useful for long-running operations or when you want to process data as it arrives, rather than waiting for the entire response. To use this, you would typically make a POST request to the /{package_name}/sse endpoint with your standard JSON-RPC payload. The server will then hold the connection open and stream back events, where each event's data field will contain a part of the JSON response. Clients should be configured to listen for these events and process them accordingly. This allows for a more responsive user experience, especially when dealing with large language models or other tools that generate output progressively.
Advanced Usage
Master Mode
Master mode allows centralized management of MCP servers using S3 storage:
# Install package for master mode (skips env prompts)
fluidmcp install package --master
# Run all servers using S3 configuration
fluidmcp run all --master
In master mode:
- Environment variables are managed via a central
.envfile - Server metadata is stored in S3 (
s3_metadata_all.json) - Automatic synchronization across multiple environments
Secure Mode
Enable secure mode for production deployments:
# Run with auto-generated token
fluidmcp run package --secure
# Run with custom token
fluidmcp run package --secure --token your-custom-token
Secure mode features:
- Bearer token authentication
- Encrypted communication between servers
- Environment variable injection for security tokens
Port Management
FluidMCP automatically manages ports to avoid conflicts:
- Scans for available ports in range 8100-9000
- Kills existing processes when using
--force-reload - Assigns unique ports to each server in multi-server setups
API Registry
FluidMCP CLI connects to the FluidMCP registry to download packages. The registry provides:
- Package versioning and metadata
- Secure package distribution via S3 pre-signed URLs
- Authentication and authorization
- Package discovery and search
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