my-portfolio-mcp
Provides tools for interacting with SurveyMonkey's API, enabling AI agents to manage surveys, collectors, and responses programmatically through the SurveyMonkey platform.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
my-portfolio-mcp
A FastMCP server (SurveyMonkey response toolkit) 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.py # the one and only server moduleNote: there is a single server module. (A previous version shipped two divergent copies —
mcp_portfolio_server.pyandsurveymonkey_mcp.py— which caused Dataiku to run the older, buggy copy. They have been consolidated.)
Related MCP server: Dataiku DSS MCP Server
Required environment variables
Variable | Required | Purpose |
| yes | Bearer token; the server refuses to start without it. |
| no | Defaults to |
| no | Defaults to |
| no | Directory (e.g. a Dataiku managed-folder path) for persisting drafts to JSON so a |
| no | Defaults to |
Set these on the Dataiku code env / agent tool, not in the repo.
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
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.
Equivalently, the installed console script mcp-portfolio-server runs the same
main() entry point.
Local sanity check (optional)
pip install -e .
SURVEYMONKEY_ACCESS_TOKEN=xxx python -m mcpsrv.mcp_portfolio_server
# should start and wait on stdin; Ctrl-C to exitTools exposed
list_surveys, list_collectors, load_survey, start_response,
save_answer, save_answers, review_response, submit_response,
get_submitted_response.
The flow is draft-based: start_response → save_answers (bulk; or save_answer
per question) → review_response → submit_response(user_confirmed=True).
Submission is a single POST /collectors/{id}/responses with the full pages
payload and response_status="completed".
Answers may be given by ID or by the human-readable choice/row/col text from
load_survey — the server resolves labels to IDs (case-insensitive) and, on a
non-match, returns an error listing the valid options. save_answers validates
each answer independently, so valid ones persist even when others fail.
Adding 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.
Tool Schema Changelog
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