manthan29-code/codescope-mcp
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CodeScope MCP provides LLMs and AI assistants with controlled, intelligent, and context-efficient access to local codebases over the using .
概要
CodeScope MCP provides LLMs and AI assistants with controlled, intelligent, and context-efficient access to local codebases over the using . Instead of naively dumping entire folders or overwhelming context windows with garbage files (node_modules, .venv, binaries, lock files), CodeScope acts as a security and context firewall, offering a layered toolset for codebase exploration, code search, and file retrieval. When using AI coding assistants or LLM chat agents over MCP, granting raw filesystem access often leads to: 1. : Giant folders like node_modules/, .git/, dist/, or .venv/ flooding prompt tokens. 2. : Reading multi-megabyte files or large lock files (package-lock.json, poetry.lock). 3. : Unintended dumping of binary or compiled files (.pyc, images, .exe, .so) into text prompts. 4. : Path traversal attacks (e.g. ../../../../etc/passwd or accessing sensitive files outside project roots). bridges this gap.
README
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🔭 CodeScope MCP
Intelligent, Token-Optimized Codebase Context MCP Server Built for Cloud Chat LLMs (Claude, ChatGPT, Gemini).
CodeScope MCP provides cloud-based Chat LLM applications with controlled, intelligent, and context-efficient access to local codebases over the Model Context Protocol (MCP) using Streamable HTTP transport.
Instead of naively dumping entire folders or overwhelming context windows with garbage files (node_modules, .venv, binaries, lock files), CodeScope acts as a security and context firewall, offering a layered toolset for codebase exploration, code search, and file retrieval.
📑 Table of Contents
- What This Is About
- Target Audience: Built for Cloud Chat LLMs (Not for AI IDEs)
- Why You Should Use It
- What It Does (Core Tools)
- Architecture Overview
- System Flow & Layered Discovery
- Project Structure
- Getting Started
- Working with Write Tools & AI Prompting
- Automated Verification & .codescope.toml Guide
- Exposing with ngrok (Remote / Cloud Setup)
- Connecting to MCP Clients
- Cross-Platform Support (macOS, Linux & Windows)
- Security & Constraints
- Running Tests
- License
💡 What This Is About
When using AI coding assistants or LLM chat agents over MCP, granting raw filesystem access often leads to:
- Context Window Blowup: Giant folders like
node_modules/,.git/,dist/, or.venv/flooding prompt tokens. - High Token Costs & Slow Responses: Reading multi-megabyte files or large lock files (
package-lock.json,poetry.lock). - Binary Corruption: Unintended dumping of binary or compiled files (
.pyc, images,.exe,.so) into text prompts. - Security Vulnerabilities: Path traversal attacks (e.g.
../../../../etc/passwdor accessing sensitive files outside project roots).
CodeScope MCP bridges this gap. It provides a standardized MCP server built on FastMCP with Streamable HTTP that delivers smart, token-efficient, .gitignore-aware file reading, fast code/regex search, and metadata analysis.
🎯 Target Audience: Built for Cloud Chat LLMs (Not for AI IDEs)
インストール
This server does not publish a one-line install command.
Open the repository installation guide MCP 初心者？mcpskill.org のガイドから始めましょう →設定
{
"mcpServers": {
"codescope": {
"url": "http://127.0.0.1:8000/mcp",
"transport": "streamable-http"
}
}
}