Python Executor MCP Server

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run_python_batch

Execute multiple independent Python snippets concurrently with parallel workers, ideal for batch file processing and data analysis tasks that don't require shared state.

Instructions

Execute multiple Python snippets in parallel (max 4 workers, max 20 snippets). Use for independent tasks: file processing, parallel data analysis. Use run_python for stateful/dependent operations. See README.md for examples.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "codes": "<codes>"
    },
    "name": "run_python_batch"
  }
}

Input Schema

Name	Required	Description	Default
`cwd`	No
`codes`	Yes
`timeout`	No
`max_workers`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 1, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses parallel execution and limits (max workers, max snippets) but does not explain error handling, result ordering, isolation, or what happens when limits are exceeded, leaving notable behavioral gaps.

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?

Three concise sentences with no fluff: first states the core function, second gives usage context, third names the alternative and points to examples. Information is front-loaded.

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?

The description covers the primary use case and points to README, but omits important operational details such as timeout behavior, cwd handling, and behavior when max limits are exceeded. The output schema may cover return values, but the tool still feels incomplete for a batch execution tool.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not explain the meaning of cwd, timeout, or max_workers beyond implicit mention of worker/snippet limits. It fails to compensate for the undocumented schema parameters.

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 'Execute multiple Python snippets in parallel' with specific constraints (max 4 workers, max 20 snippets), distinguishing it from the sibling run_python tool by emphasizing the batch/parallel aspect.

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

Usage Guidelines5/5

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

It explicitly states when to use this tool ('independent tasks: file processing, parallel data analysis') and when not to ('Use run_python for stateful/dependent operations'), providing clear guidance and naming the alternative.

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

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