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MCP Python Interpreter

MCP Python Interpreter

by PortPro-Technologies-Inc
OverviewSchemaRelated ServersDiscussionsAdmin
Python

list_installed_packages

Identify installed Python packages in a specific environment using the MCP Python Interpreter. Specify the environment name to retrieve package details effectively.

Instructions

List installed packages for a specific Python environment.

Args:
    environment: Name of the Python environment (default: default if custom path provided, otherwise system)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_installed_packages"
  }
}

Input Schema

NameRequiredDescriptionDefault
environmentNodefault

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations provided, so description should fully disclose behavior. However, it does not detail what constitutes installed packages, return format, or prerequisites, only a vague default logic for environment parameter.

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?

Extremely concise with one sentence and a parameter description, no unnecessary words.

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?

Given the tool's simplicity (one optional param, no output schema, no annotations), the minimal description might suffice but lacks details like listing all packages globally or per environment, version info, and how to specify custom paths.

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?

Only one parameter, schema description coverage is 0%, but the description adds some meaning by explaining default behavior ('default if custom path provided, otherwise system'). Still lacks full clarity on how environment is resolved.

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?

Description clearly states 'List installed packages for a specific Python environment' with a verb and resource, and distinguishes from siblings like install_package and list_python_environments.

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?

Implies usage for listing packages in a Python environment but provides no explicit when-to-use or when-not-to-use guidance, nor alternatives.

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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