A command-line tool to run MCP servers from a single file, with support for automatic dependency resolution, environment setup, and package installation from local or S3 sources.
๐ฆ Package Management
fluidmcp install author/package@version
๐ FastAPI Gateway
/docs
๐ Security & Authentication
pip install fluidmcp
fluidmcp install author/package@version
fluidmcp list
fluidmcp run ./config.json --file
fluidmcp file_directory/config.json --file --secure --token your_token --start-server
fluidmcp run "https://bucket.s3.amazonaws.com/config.json" --s3
fluidmcp run "https://bucket.s3.amazonaws.com/config.json" --s3
Common Options:
--start-server
โ Starts FastAPI server--master
โ Use S3-driven config--file
โ Run from local config.json--s3
โ Run from S3 URL--secure
โ Enable secure token mode--token <token>
โ Custom bearer tokenfluidmcp run author/package@version --start-server
fluidmcp run all
fluidmcp install author/package@version --master
fluidmcp run all --master
# S3 Credentials (used in --master mode)
export S3_BUCKET_NAME="..."
export S3_ACCESS_KEY="..."
export S3_SECRET_KEY="..."
export S3_REGION="..."
# Registry access
export MCP_FETCH_URL="https://registry.fluidmcp.com/fetch-mcp-package"
export MCP_TOKEN="..."
fluidmcp edit-env <author/package@version>
.fmcp-packages/
โโโ Author/
โโโ Package/
โโโ Version/
โโโ metadata.json
โโโ [tool files]
{
"mcpServers": {
"maps": {
"command": "npx",
"args": ["-y", "@package/server"],
"env": {
"API_KEY": "xxx"
}
}
}
}
fluidmcp install Google_Maps/google-maps@0.6.2
fluidmcp run all
Then call it using:
import requests, json
url = "http://localhost:8099/google-maps/mcp"
payload = {
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "maps_search_places",
"arguments": {
"query": "coffee shops in San Francisco"
}
}
}
response = requests.post(url, json=payload)
print(json.dumps(response.json(), indent=2))
curl -N -X POST http://localhost:8099/package/sse \
-H "Content-Type: application/json" \
-d @payload.json
sse/start
sse/stream
sse/message
sse/tools_call
Useful for LLMs, web scraping, or AI workflows that stream data.
FluidMCP is open for collaboration. Feel free to open issues or submit PRs.
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