python-session-mcp

Run Python in a session that stays alive, and expose it to LLM clients over the Model Context Protocol.

Author: Dr Merwan Roudane

What makes it different

The session persists. A DataFrame loaded in one call is still there in the next, so an analysis is built up in steps rather than resent whole each time.

Your interpreter, not the server's. The server may well be installed under a bare Python with no pandas in it. The interpreter that runs your code is chosen separately: a conda or Anaconda installation is preferred when one is present, and PYTHON_MCP_INTERPRETER overrides that.

A crash costs one process. Code runs in a worker, not in the server. Exhaust memory, call sys.exit, crash a C extension — the worker is replaced and the server carries on. It also keeps user code away from the server's stdin, which under MCP is the JSON-RPC stream itself.

Related MCP server: LLM Python Code Sandbox

Install

pip install python-session-mcp

Library use

from python_mcp import PythonRunner

with PythonRunner() as py:
    py.run("import pandas as pd, statsmodels.api as sm")
    py.run("df = pd.read_csv('macro.csv')")
    print(py.run("df.describe()"))

    py.run("m = sm.OLS(df['y'], sm.add_constant(df[['x','z']])).fit()")
    print(py.run("m.summary()"))
    print(py.value("m.params.to_dict()"))    # a real Python dict

A final expression is shown the way a REPL would, so df.head() on its own displays the frame without print().

MCP server use

{
  "mcpServers": {
    "python": {
      "command": "python-session-mcp",
      "env": { "PYTHON_MCP_INTERPRETER": "C:\\Users\\you\\anaconda3\\python.exe" }
    }
  }
}

Tools

Session

Running code

Data

Estimation

Charts

Errors

Failures name the exception and the line of your code, and leave the session intact:

Python error: NameError on line 2: name 'undefined_name' is not defined

Anything printed before the failure is reported with it, since that output is often what explains the failure.

Figures

A plot cannot come back as text. Draw it, then save it:

py.run("import matplotlib; matplotlib.use('Agg')")
py.run("import matplotlib.pyplot as plt; plt.plot(df['x'], df['y'], 'o')")
py.save_figure("figures/scatter.png")     # missing folders are created

Worth knowing

• The interpreter is separate from the server's. Check python_status before relying on a package being there.

• State is a convenience and a hazard. Names persist, so a stale variable from an earlier step can quietly feed a later one. reset_namespace when starting something new.

• run_python executes whatever it is given, in your environment, with your file access. That is the point of it, and worth being deliberate about.

• The final expression is echoed. A long DataFrame will print in full unless you slice it.

Tests

python tests/test_live.py     # 41 tests

They cover persistence, error reporting with line numbers, JSON round trips, figure writing, recovery after user code kills the worker outright, and every data and estimation tool against generated data with known coefficients.

Checked against EViews on the same data, the regression agrees to every printed digit -- coefficients, R-squared, Durbin-Watson, and the Breusch-Godfrey and Jarque-Bera statistics alike.

Licence

MIT. Copyright (c) 2026 Merwan Roudane.