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MCP Code Mode

MCP Code Mode

by anaseqal
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

pip_install

Pre-install Python packages before code execution to ensure dependencies are available.

Instructions

Pre-install a Python package.

Use this to install packages before execution if you know you'll need them, or if auto-install missed something.

Args: package_name: The pip package name to install

Returns: Installation result

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": {
      "package_name": "<package_name>"
    },
    "name": "pip_install"
  }
}

Input Schema

NameRequiredDescriptionDefault
package_nameYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.5

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions 'Installation result' but lacks details on side effects (e.g., environment changes, permissions, failure modes). Minimal behavioral disclosure.

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 concise (3 sentences) with a clear structure: purpose, usage guidance, and parameter/return sections. No extraneous content.

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?

For a simple 1-parameter tool with an output schema, the description covers core purpose and usage. However, it omits error handling, return format, and potential prerequisites, leaving gaps for an agent.

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?

With 0% schema description coverage, the description adds basic meaning to 'package_name' as 'The pip package name to install', but does not elaborate on format or version specifics. Adequate but not rich.

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 action ('Pre-install a Python package') and the resource (Python package). It distinguishes from sibling tools like 'run_python' by specifying installation.

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?

Provides explicit guidance on when to use ('if you know you'll need them, or if auto-install missed something'), but does not mention when not to use or name alternative tools.

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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