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Python REPL MCP Server

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Python REPL MCP Server

This MCP server provides a Python REPL (Read-Eval-Print Loop) as a tool. It allows execution of Python code through the MCP protocol with a persistent session.

Setup

No setup needed! The project uses uv for dependency management.

Related MCP server: Python REPL MCP Server

Running the Server

Simply run:

uv run src/python_repl/server.py

Usage with Claude Desktop

Add this configuration to your Claude Desktop config file:

{
  "mcpServers": {
    "python-repl": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/python-repl-server",
        "run",
        "mcp_python"
      ]
    }
  }
}

The server provides three tools:

  1. execute_python: Execute Python code with persistent variables

    • code: The Python code to execute

    • reset: Optional boolean to reset the session

  2. list_variables: Show all variables in the current session

  3. install_package: Install a package from pypi

Examples

Set a variable:

a = 42

Use the variable:

print(f"The value is {a}")

List all variables:

# Use the list_variables tool

Reset the session:

# Use execute_python with reset=true

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. Here are some ways you can contribute:

  • Report bugs

  • Suggest new features

  • Improve documentation

  • Add test cases

  • Submit code improvements

Before submitting a PR, please ensure:

  1. Your code follows the existing style

  2. You've updated documentation as needed

  3. Maybe write some tests?

For major changes, please open an issue first to discuss what you would like to change.

Available Tools

3 tools
execute_pythonB

Execute Python code and return the output. Variables persist between executions.

ParametersJSON Schema
NameRequiredDescriptionDefault
codeYesPython code to execute
resetNoReset the Python session (clear all variables)

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must disclose behavioral traits on its own. It does state that variables persist between executions, which is a key stateful behavior. However, it omits other critical aspects such as error handling, output format, sandboxing, timeouts, or potential side effects, making the behavior of arbitrary code execution largely opaque.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, with the main action 'Execute Python code' front-loaded. Every word serves a purpose, and there is no redundant or tangential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool that executes arbitrary code, this description is underspecified. It does not explain what 'output' includes (stdout, stderr, exceptions), nor does it address side effects, resource limits, or session behavior beyond persistence. Since there is no output schema, the description should have elaborated further, but it leaves major gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters (code and reset) are fully described in the schema. The description adds no additional parameter semantics, but per the rubric, the high schema coverage warrants a baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's function: executing Python code and returning output. This specific verb+resource combination distinguishes it from sibling tools like list_variables and install_package, which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when one needs to run Python code, but it provides no explicit guidance on when to use this tool vs. alternatives. It does not mention list_variables or install_package or any exclusion conditions, leaving the usage context somewhat implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

install_packageB

Install a Python package using uv

ParametersJSON Schema
NameRequiredDescriptionDefault
packageYesPackage name to install (e.g., 'pandas')

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure, but it only says 'Install a Python package using uv'. It does not mention side effects such as modifying the environment, requiring network access, or how conflicts are resolved. The mention of 'uv' adds a detail about the package manager but lacks consequential behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that immediately communicates the tool's purpose. It contains no unnecessary words or fluff, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool, the description provides the core action and method, but it lacks usage guidelines and behavioral transparency. Given the absence of annotations, the description is not fully complete, though it covers the basics for a basic install operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already fully describes the single parameter with 100% coverage, including an example ('pandas'). The description adds no additional semantic value beyond the schema, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Install') the resource ('a Python package') and the method ('using uv'). It distinguishes itself from sibling tools like execute_python and list_variables by indicating a package installation operation rather than code execution or variable inspection.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention situations such as needing to add a dependency, nor does it exclude using execute_python or list_variables for other tasks. The absence of any usage context or alternative comparisons leaves the agent without clear decision-making support.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_variablesB

List all variables in the current session

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must convey behavioral traits. It implies a read-only listing but does not state whether values are included, how the result is returned, or if there are side effects. 'Current session' is ambiguous and not elaborated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, direct sentence with no wasted words. It is front-loaded and appropriately sized for a zero-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema and no annotations, so the description should explain what the output looks like. It only says 'list all variables', leaving unclear whether the output is names only or names with values, and what format is used. For a simple tool this might be sufficient, but it lacks completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema is trivially complete. The description does not need to explain parameter details; the baseline of 4 applies because there is nothing to clarify.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'List' and a clear resource 'variables', scoped to 'current session'. It obviously differs from sibling tools like execute_python and install_package, so purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided about when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or comparison with execute_python or install_package. The description only states the action, not the context of use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updates
    • First observedexecute_python
    • First observedinstall_package
    • First observedlist_variables
Install Server
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TDQS

B3.4/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: execute_python runs code, install_package manages dependencies, and list_variables inspects the session state. An agent can easily tell them apart as they target different aspects of the Python REPL workflow.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern (execute_python, install_package, list_variables) with clear, descriptive names. The naming convention is uniform throughout the set, making it predictable and easy to understand.

Tool Count3/5

With only 3 tools, the set feels thin for a Python REPL server, as it lacks operations like uninstalling packages, clearing variables, or handling errors. While the core functions are covered, the count is borderline low for the domain's typical scope.

Completeness3/5

The tools cover basic execution, package installation, and variable listing, but there are notable gaps: no way to update or remove packages, delete variables, or manage session state beyond listing. This could cause agent failures in more complex workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

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