CodeWeaver MCP Server

Overview

CodeWeaver is a comprehensive Model Context Protocol server that enables semantic code navigation and search using natural language queries. It provides a powerful, extensible framework with support for multiple embedding providers and vector database backends.

Supported Embedding Providers

• Voyage AI: High-quality embeddings for code and text
• OpenAI: GPT-based embeddings for semantic understanding
• Cohere: Multilingual embeddings with strong code support
• HuggingFace: Open-source embedding models

Supported Vector Databases

• Qdrant: High-performance vector search engine
• Pinecone: Managed vector database
• Weaviate: Open-source vector database with GraphQL
• ChromaDB: Embedded vector database

Features

Semantic Code Search

• Natural language code queries
• Find similar code patterns
• Discover relevant functions and classes
• Cross-file dependency analysis
• API usage examples

Code Navigation

• Jump to definitions using natural language
• Find all references semantically
• Explore code structure intuitively
• Navigate by intent, not syntax

Multi-Language Support

• Python, JavaScript, TypeScript
• Java, C++, C#, Go, Rust
• And more programming languages

Extensible Architecture

• Plugin system for custom embeddings
• Custom vector database backends
• Configurable indexing strategies
• Fine-tuning options

Natural Language Interface

• Ask questions about codebases
• Generate documentation
• Understand code functionality
• Find implementation examples

Use Cases

• Codebase exploration and onboarding
• Finding relevant code examples
• Understanding legacy code
• Refactoring assistance
• Documentation generation
• Code review automation
• Technical debt analysis
• API usage discovery

Technical Implementation

Built as a full-stack MCP platform with a powerful server, extensible framework, and natural language interface. Supports both local and cloud-hosted embedding providers and vector databases.

Performance

Optimized indexing strategies with incremental updates, efficient embedding caching, and vector database query optimization for fast semantic search across large codebases.

Integration

Compatible with Claude Desktop, Cursor, VS Code, and other MCP clients. Supports both CLI and programmatic configuration for embedding and database selection.