Skip to main content
Glama
anaseqal

MCP Code Mode

by anaseqal

get_system_context

Retrieve system environment details including OS, Python version, installed libraries, and execution mode before generating code. Helps tailor code to the current setup and avoid errors.

Instructions

Get comprehensive system context before writing code.

CALL THIS FIRST to understand:

  • OS, Python version, available paths

  • Installed and available libraries

  • Execution mode (direct or Docker sandbox)

  • Past learnings from errors

  • Tips for writing effective code

Returns detailed system information formatted for code generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so description carries full burden. It details what information is returned (OS, Python version, paths, libraries, execution mode, past learnings, tips), making behavior fully transparent.

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?

Description is concise: one sentence followed by a bulleted list of what it provides. Every sentence adds value, and structure is front-loaded with the main purpose.

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

Completeness5/5

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

Given zero parameters and an output schema, the description fully covers the tool's context. It explains what the tool returns and its recommended usage, leaving no gaps.

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?

Input schema has 0 parameters, so schema coverage is 100%. According to guidelines, 0 params warrants a baseline of 4. Description adds no parameter info, which is appropriate.

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: 'Get comprehensive system context before writing code.' It uses specific verb+resource and distinguishes itself from siblings like run_python and pip_install.

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?

Explicitly says 'CALL THIS FIRST,' indicating when to use it. While no alternatives or exclusions are given, the context makes it clear that this is the initial step. Siblings are distinct tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/anaseqal/codemode'

If you have feedback or need assistance with the MCP directory API, please join our Discord server