Orgo MCP Server

orgo_exec

Destructive

Execute Python code on virtual computers to process data, manipulate files, and run scripts through the Orgo MCP Server.

Instructions

Execute Python code on the computer.

Returns the output of the execution. Useful for data processing,
file manipulation, and quick scripts.

Args:
    params (ExecInput): Input containing:
        - computer_id (str): Computer ID
        - code (str): Python code to execute
        - timeout (int): Max execution time, 1-300 seconds (default: 30)

Returns:
    str: Python execution output

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": {
      "params": "<params>"
    },
    "name": "orgo_exec"
  }
}

Input Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 9, 2026

TDQS

A4.2/5.0

Behavior3/5

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

Annotations already indicate destructiveHint=true (implying side effects) and readOnlyHint=false (implying mutations), which the description aligns with by mentioning 'Execute Python code' and 'data processing, file manipulation'. The description adds context about timeout behavior and output format, but does not disclose additional traits like security implications, environment isolation, or error handling beyond what annotations provide.

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 well-structured and front-loaded with the core purpose, followed by usage context, parameter details, and return information. Every sentence adds value without redundancy, making it efficient and easy to parse.

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

Completeness4/5

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

Given the tool's complexity (executing arbitrary code with destructive potential) and rich annotations/output schema, the description is mostly complete. It covers purpose, usage, parameters, and returns, but could improve by addressing safety warnings, execution environment details, or error scenarios to fully guide an agent in high-stakes contexts.

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?

With 0% schema description coverage, the description fully compensates by detailing all parameters (computer_id, code, timeout) in the Args section, including their purposes and default values. This adds significant meaning beyond the schema, though it could benefit from more context on computer_id usage or code constraints.

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 tool's purpose with a specific verb ('Execute Python code') and resource ('on the computer'), distinguishing it from sibling tools like orgo_bash (for shell commands) and orgo_ai_completion (for AI completions). The title 'Execute Python Code' reinforces this clarity.

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

Usage Guidelines4/5

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

The description provides clear context for usage ('Useful for data processing, file manipulation, and quick scripts'), but does not explicitly state when not to use it or name specific alternatives among siblings (e.g., orgo_bash for non-Python tasks). This gives good guidance but lacks explicit exclusions.

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