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Uten Agent Terminal MCP

Uten Agent Terminal MCP

by dev-vieester
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Python File

python_file

Execute a Python script file and return its output, enabling AI agents to run Python code through the MCP terminal server.

Instructions

This function will execute a python file and return the 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": {
      "file": "<file>"
    },
    "name": "python_file"
  }
}

Input Schema

NameRequiredDescriptionDefault
fileYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations, the description must disclose behavioral traits but only states the basic execute-and-return-output behavior. It does not mention side effects, sandboxing, interpreter requirements, error handling, or whether the file is run in an isolated environment, which are important for a code execution tool.

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 filler. It is front-loaded with the action and resource, making it easy to scan.

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?

The tool has no output schema and no annotations, so the description must provide essential context. It fails to cover return format details (stdout vs. full output), execution semantics, error behavior, or any safety-related context. For a code execution tool, this is a significant gap.

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 explicitly explain what the 'file' parameter should contain. It implies a path to a Python file, but does not state whether relative paths are allowed, what file types are accepted, or any format constraints. The description adds minimal meaning beyond the raw schema.

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

Purpose4/5

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

The description states a specific verb and resource: 'execute a python file' and 'return the output.' This clearly communicates the core function and differentiates from file-read/write tools, though it does not explicitly contrast with the sibling tool python_code.

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 given about when to use this tool versus alternatives like python_code or bash. There is no mention of prerequisites, working directory, or conditions that make this tool the appropriate choice.

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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