my-portfolio-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@my-portfolio-mcpshow my portfolio summary"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
my-portfolio-mcp
A FastMCP server packaged so it installs into a Dataiku code env's site-packages,
which is both readable by the impersonated run-as user and on PYTHONPATH.
Layout
my_portfolio_mcp/
├── pyproject.toml
├── README.md
└── mcpsrv/
├── __init__.py
└── mcp_portfolio_server.pyRelated MCP server: mymcpservercli
Install into the Dataiku code env
Push this folder to a git repo your Dataiku instance can reach.
In Dataiku: Administration → Code envs → DJH_Local_MCP → Packages to install, add one line:
git+https://YOUR-GIT-HOST/you/my-portfolio-mcp.git(Or build a wheel —
python -m build— and reference that instead.)Update / rebuild the code env.
Configure the Dataiku agent tool
command:
pythonargs (two separate entries):
-mmcpsrv.mcp_portfolio_server
That's it. Because the package is now in site-packages, -m resolves it and the
file is readable — clearing both the ModuleNotFoundError and the Errno 13
permission error.
Local sanity check (optional)
pip install -e .
python -m mcpsrv.mcp_portfolio_server # should start and wait on stdin; Ctrl-C to exitAdding more tools
Add more @mcp.tool()-decorated functions in mcp_portfolio_server.py. Use
absolute paths or package-relative imports inside them — the tool subprocess runs
from a throwaway temp working directory, so bare relative paths will break.
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