Click on "Deploy 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., "@MCP Stats Tools Server Run a paired significance test on these two samples and report the p-values"
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.
An MCP server with three statistical tools that an agent can call:
survival_analysis: Kaplan-Meier curves, plus a log-rank test when exactly two
groups are given
compare_two_samples: paired t-test, Wilcoxon signed-rank test and a bootstrap
CI on the mean difference
check_distribution_drift: Kolmogorov-Smirnov test and Population Stability Index
between a reference and a current sample
No API keys or paid services. The tools are plain statistics, not model calls.
pip install -r requirements.txt
make test # 18 tests
make evaluate # quality, latency and cost report
make run # server over stdioTo poke at it with the MCP Inspector:
npx @modelcontextprotocol/inspector python -m app.serverRelated MCP server: MCP ML Monitor
app/tools.py the statistics, no MCP imports
app/server.py MCP tool definitions
app/evaluate.py evaluation report
tests/ unit tests for tools.py and in-process tests through the serverThe logic in tools.py raises ValueError on bad input. server.py converts that
to ToolError, because the SDK treats any other exception as an unexpected crash
and the client never sees the message.
Tool functions return dict[str, Any] rather than a bare dict. With a bare
dict the SDK can't build an output schema and results only come back as text,
not structured_content.
python -m app.evaluate prints three sections.
Quality. Inputs with a known answer: two groups with different hazards should
give a significant log-rank test and two identical ones should not, a 10-unit
shift should be recovered by compare_two_samples, and drift should be flagged for
a shifted sample but not a stable one.
Latency. 50 calls per tool through server.call_tool, so argument validation
and serialisation are included. On my machine drift takes about 1 ms, the paired
comparison about 9 ms and survival analysis about 40 ms (it bootstraps).
Cost. The tools run locally, so there's no bill to read from. The report
estimates monthly compute cost from mean latency, a daily call volume and an
assumed per-vCPU-hour rate set at the top of evaluate.py.
Only stdio transport is used. HTTP isn't exercised.
The tests call the server in-process. Nothing here connects a real agent to it.
The cost figure is an estimate, not a measured bill.
This server cannot be deployed
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