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., "@python-runner run Python code to calculate the first 10 prime numbers"
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 (Model Context Protocol) server that exposes a run_script tool for executing Python code via subprocess.
uv syncRelated MCP server: Python Executor MCP Server
uv run python server.pyclaude mcp add --transport stdio --scope user python-runner -- uvx --from git+https://github.com/Drahoxx/python-mcp python-mcpOr for a local installation:
claude mcp add --transport stdio --scope user python-runner -- uv run --directory /path/to/python-mcp python-mcpAdd to ~/.config/opencode/opencode.json:
{
"mcpServers": {
"python-runner": {
"command": "uvx",
"args": ["--from", "git+https://github.com/Drahoxx/python-mcp", "python-mcp"]
}
}
}Or for a local installation:
{
"mcpServers": {
"python-runner": {
"command": "uv",
"args": ["run", "--directory", "/path/to/python-mcp", "python-mcp"]
}
}
}Add to your MCP client configuration:
{
"mcpServers": {
"python-runner": {
"command": "uv",
"args": ["run", "--directory", "/path/to/python-mcp", "python", "server.py"]
}
}
}run_scriptExecute Python code and return the result.
Name | Type | Default | Description |
|
| required | Python code to execute |
|
|
| Execution timeout in seconds |
Returns a JSON string with:
Field | Type | Description |
|
| Whether execution succeeded (exit code 0) |
|
| Captured standard output |
|
| Captured standard error |
|
| Error message/traceback if failed |
|
| Process exit code (-1 for timeout) |
Successful execution:
run_script('print("hello")')
# {"success": true, "stdout": "hello\n", "stderr": "", "error": null, "return_code": 0}Syntax error:
run_script('print(')
# {"success": false, "stdout": "", "stderr": "...", "error": "SyntaxError...", "return_code": 1}Runtime error:
run_script('1/0')
# {"success": false, "stdout": "", "stderr": "...", "error": "ZeroDivisionError...", "return_code": 1}Timeout:
run_script('import time; time.sleep(60)', timeout=2)
# {"success": false, "stdout": "", "stderr": "", "error": "Execution timed out after 2 seconds", "return_code": -1}uv run pytestuv run pytest --cov=serverExecute Python code and return the result.
Args: code: Python code to execute timeout: Execution timeout in seconds (default: 30)
Returns: JSON string with success, stdout, stderr, error, and return_code
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| timeout | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return format (success, stdout, stderr, error, return_code) and the timeout default, but does not mention side effects, security implications, sandboxing, or restrictions on the executed code. For arbitrary code execution, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficient, containing only a purpose statement, an Args section, and a Returns section. It is well-structured and front-loaded with the main verb phrase, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with two parameters and a clear return contract, the description is reasonably complete. However, it lacks usage guidelines, behavioral caveats, and examples, which are important for a code execution tool with no annotations. The return format is described, but the absence of any mention of execution environment or safety considerations makes it less complete than ideal.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by explicitly listing parameters with concise explanations: 'Python code to execute' and 'Execution timeout in seconds (default: 30)'. This adds meaning beyond the bare property names and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool executes Python code and returns a result, using the specific verb 'execute' and resource 'Python code'. It is distinct and unambiguous, despite having no sibling tools to differentiate from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, nor does it mention any prerequisites or context for invocation. Since there are no sibling tools, this is not critical, but the absence of any usage context means the agent must infer suitability from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0run_scriptOnly one tool exists, so there is no possibility of confusion between tools. The tool's purpose is clearly defined and unambiguous.
The single tool uses a consistent verb_noun naming convention (run_script), which is clear and predictable.
The server has exactly one tool, which is slightly below the typical 3-15 range, but it directly matches the server's stated purpose of running Python code. It could potentially benefit from additional tools like environment management, but the core functionality is well-covered.
The tool covers the full lifecycle of executing a Python script, including timeout and capturing stdout/stderr/errors. For the narrow domain of running Python code, it is complete.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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Security + bug + perf + refactor audit for Python. Returns 0-10 score + MD report.
Proves AI-generated Python does what you asked: lint, types, security, sandbox run, exact fixes.
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