context-optimizer-mcp-server

🥈Silver

A Model Context Protocol (MCP) server that provides context optimization tools for AI coding assistants including GitHub Copilot, Cursor AI, Claude Desktop, and other MCP-compatible assistants enabling them to extract targeted information rather than processing large terminal outputs and files wasting their context.

5750Updated 1mo ago

Intermediate30min to implementautomation

Saves ~75 min per use

Quick InstallView Source

git clone https://github.com/malaksedarous/context-optimizer-mcp-server.git

Works with:

Claude

Overview

About This Skill

How to Use

[{"step":"Install and configure the context-optimizer-mcp-server in your MCP-compatible environment (GitHub Copilot, Cursor AI, or Claude Desktop).","action":"Follow the server's installation guide to set up the MCP connection. Ensure your IDE or terminal has access to the repository you want to analyze."},{"step":"Specify your target information using the prompt template. Replace [TARGET_INFORMATION] with what you need (e.g., 'error handling logic', 'API endpoints', 'database schema').","action":"Be specific about what you're looking for. For example: 'Find all error handling patterns in the authentication module' or 'Extract the database schema for the orders table'."},{"step":"Run the optimized context extraction and review the structured output.","action":"The server will return context in JSON format with relevant functions, variables, and recent changes. Use this as input for your AI coding assistant to ensure it focuses on the right parts of your codebase."},{"step":"Iterate with follow-up queries if needed. Use the output to refine your next query.","action":"If the initial output is too broad, add more specific criteria like 'only functions that handle 404 errors' or 'variables declared in the last 30 days'."},{"step":"Integrate the optimized context into your development workflow.","action":"Paste the output directly into your AI coding assistant's context window or save it as a reference file. This prevents the assistant from wasting context on irrelevant code."}]

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/malaksedarous/context-optimizer-mcp-server

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use the context-optimizer-mcp-server to extract the most relevant [TARGET_INFORMATION] from [SOURCE]. Focus on [SPECIFIC_CRITERIA] and ignore unrelated noise. Return only the optimized context in a structured format suitable for direct use by an AI coding assistant.

Example Output

```json
{
  "relevant_functions": [
    {
      "name": "calculateTax",
      "file": "src/utils/taxCalculator.js",
      "lines": "12-25",
      "description": "Computes tax based on income brackets and deductions",
      "parameters": ["income", "deductions", "filingStatus"],
      "return_type": "number"
    },
    {
      "name": "applyDiscount",
      "file": "src/services/orderService.js",
      "lines": "45-60",
      "description": "Applies percentage discount to order total",
      "parameters": ["orderId", "discountPercent"],
      "return_type": "Promise<Order>"
    }
  ],
  "key_variables": [
    {
      "name": "TAX_BRACKETS",
      "file": "src/constants/taxConstants.js",
      "value": "{ 'single': [0, 10275, 0.1], 'married': [0, 20550, 0.12] }",
      "usage": "Referenced in calculateTax function"
    }
  ],
  "recent_changes": [
    {
      "file": "src/models/User.js",
      "change": "Added 'lastLogin' field to track user activity",
      "date": "2024-05-15",
      "author": "dev-team"
    }
  ]
}
```

The optimized context above was extracted from a 500-line repository containing 47 files. The MCP server identified 2 relevant functions, 1 key constant, and 1 recent change that directly relate to tax calculation and order processing - the exact areas the user was debugging. By filtering out 95% of the repository's content, the AI coding assistant can now focus its context on these critical elements rather than processing irrelevant files like configuration settings, test suites, or documentation.