Axon.MCP.Server

Model Context Protocol (MCP) Server for AI IDEs - Cursor, AntiGravity & Claude

Transform your codebase into an intelligent knowledge base for AI-powered development with Cursor IDE, Google AntiGravity, and MCP-enabled assistants

📋 Table of Contents

• The Problem
• The Solution
• See It In Action
• Architecture Overview
• MCP Tools for AI Assistants
• Key Features
• Quick Start
• Use Cases
• Documentation
• Development
• Roadmap
• Contributing
• License

🎯 The Problem

Modern codebases are complex labyrinths—thousands of files, intricate dependencies, and evolving architectures. AI assistants like ChatGPT and Claude are brilliant... but they're flying blind. Without deep understanding of your codebase's structure, relationships, and patterns, they can only see the trees, never the forest.

💡 The Solution

Axon.MCP.Server transforms your entire codebase into an intelligent, queryable knowledge base using the Model Context Protocol (MCP). Think of it as giving your AI assistant X-ray vision into your code—understanding not just syntax, but semantics, architecture, and relationships.

Why Axon Stands Out

• 🧠 Semantic Understanding: Goes beyond grep to understand what code means, not just what it says
• 🔗 Relationship Mapping: Automatically builds call graphs, inheritance trees, and dependency networks
• 🤖 AI-Native Integration: Built specifically for ChatGPT, Claude, Cursor IDE, and other MCP-enabled tools
• 📊 Multi-Language Mastery: Deep analysis for C# (Roslyn), Python, JavaScript, TypeScript
• 🔍 Vector-Powered Search: Find code by meaning using semantic embeddings
• 🏗️ Architecture Intelligence: Auto-detects services, APIs, Entity Framework mappings, design patterns
• ⚡ Production-Ready Performance: <500ms p95 latency, handles 10,000+ files with ease
• 🔐 Enterprise Security: JWT auth, RBAC, audit logging, rate limiting—not a toy project

� See It In Action

🤖 AI IDE Integration - The Main Use Case

Axon MCP Server seamlessly integrates with leading AI-powered IDEs to supercharge your development workflow

Google AntiGravity - Best AI IDE for Vibe Coders

Axon MCP providing deep code context to Google AntiGravity for intelligent code assistance

Cursor IDE - AI-First Code Editor

Real-time code intelligence powered by Axon's semantic understanding in Cursor

🏛️ Management Dashboard

Real-time monitoring of code analysis and synchronization

🛠️ Architecture Visualization

Auto-generated service dependency diagrams

🏗️ Architecture Overview

10-Service Microarchitecture

┌─────────────────────────────────────────────────────────────┐
│  Client Layer: AI Assistants, IDEs, React UI, REST Clients │
└─────────────────────────────────────────────────────────────┘
                            │
            ┌───────────────┼───────────────┐
            │               │               │
┌───────────▼────┐  ┌──────▼──────┐  ┌────▼──────┐
│  MCP Server    │  │  REST API   │  │  React UI │
│    :8001       │  │    :8080    │  │    :80    │
└───────┬────────┘  └──────┬──────┘  └───────────┘
        │                  │
        │         ┌────────┼────────┐
        │         │        │        │
        │    ┌────▼───┐ ┌─▼────┐ ┌─▼─────────┐
        │    │ Worker │ │ Beat │ │ Enrichment│
        │    │ (Sync) │ │ Sched│ │  Worker   │
        │    └────┬───┘ └──────┘ └─────┬─────┘
        │         │                     │
    ┌───▼─────────▼─────────────────────▼──────┐
    │  Analysis: Tree-sitter + Roslyn + EF     │
    └───────────────────┬──────────────────────┘
                        │
        ┌───────────────┼───────────────┐
        │               │               │
┌───────▼──────┐  ┌────▼────┐  ┌──────▼────────┐
│ PostgreSQL   │  │  Redis  │  │ Prometheus +  │
│ + pgvector   │  │ Cache   │  │   Grafana     │
└──────────────┘  └─────────┘  └───────────────┘

Technology Stack

Backend: FastAPI, Celery, SQLAlchemy 2.0 (async), Python 3.11+
Parsing: Tree-sitter (multi-lang), Roslyn (C# semantic analysis)
Database: PostgreSQL 15 + pgvector, Redis
AI/ML: OpenAI/OpenRouter (LLM), sentence-transformers (embeddings)
Frontend: React + TypeScript, Vite
Infrastructure: Docker Compose, Prometheus, Grafana

🔌 MCP Tools for AI Assistants

The server exposes 12 powerful tools to AI assistants via the Model Context Protocol:

✨ Key Features

🔬 1. Hybrid Python/C# Analysis Engine

• Tree-sitter: Lightning-fast syntactic parsing for Python, JavaScript, TypeScript, and C#
• Roslyn Subprocess: Compiler-grade semantic analysis for C# (type resolution, cross-file references, metadata)
• EF Core Analyzer: Extracts Entity Framework entities, table mappings, and relationships automatically

🗂️ 2. Intelligent Code Indexing

• 🔄 Automatically discovers repositories from GitLab/Azure DevOps
• 🏷️ Extracts symbols (classes, functions, variables) with rich metadata (docstrings, parameters, return types)
• 📞 Builds call graphs, inheritance hierarchies, and import dependency maps
• 🏗️ Detects services, APIs, workers, and libraries with auto-classification
• 🧮 Generates vector embeddings for semantic search powered by pgvector

🤖 3. AI-Powered Enrichment

• 📝 LLM-generated summaries for symbols and modules (using OpenRouter/OpenAI)
• ⚡ Parallel processing with 8-worker Celery pipeline for blazing speed
• 💾 Smart caching to avoid re-generation and reduce API costs

📊 4. Production-Grade Observability

• 📈 Pre-configured Prometheus metrics (API latency, sync status, search performance, cache hit rates)
• 📉 Beautiful Grafana dashboards for real-time monitoring
• 📄 Structured JSON logging with structlog for easy parsing
• 📡 Real-time sync progress via Redis Pub/Sub for responsive UI updates

🔐 5. Enterprise Security

• 🔑 JWT authentication + API keys for flexible auth strategies
• 👥 Role-based access control (admin, readonly) for granular permissions
• 🍪 HTTP-only cookies with CSRF protection
• ⏱️ Rate limiting and comprehensive audit logging

📊 Data Model Highlights

The system maintains a rich relational model:

• Repositories: Source control repos (GitLab/Azure DevOps)
• Files: Source code files with content hashes
• Symbols: Functions, classes, variables with AI enrichment
• Relations: Inherits, implements, calls, references, imports
• Services: Detected APIs, workers, libraries
• EfEntities: Entity Framework → database table mappings
• Embeddings: pgvector embeddings for semantic search
• Chunks: Code chunks (function/class level) for RAG

Total Tables: 14 with optimized indexes, cascading deletes, unique constraints

🚀 Quick Start

Get up and running in 5 minutes with Docker Compose.

Prerequisites

• 🐳 Docker & Docker Compose installed
• 🐍 Python 3.11+ (for local development)
• 🔑 GitLab or Azure DevOps access token

Step 1: Clone & Configure

# Clone the repository
gi