semantic-code-mcp

Semantic Code MCP

AI-powered semantic code search for coding agents. An MCP server that indexes your codebase with vector embeddings so AI assistants can find code by meaning, not just keywords.

Ask "where do we handle authentication?" and find code that uses login, session, verifyCredentials — even when no file contains the word "authentication."

Why

Traditional grep and keyword search break down when you don't know the exact terms used in the codebase. Semantic search bridges that gap:

• Concept matching — "error handling" finds try/catch, onRejected, fallback patterns
• Typo-tolerant — "embeding modle" still finds embedding model code
• Context-aware chunking — AST-based (Tree-sitter) or smart regex splitting preserves code structure
• Fast — progressive indexing lets you search while the codebase is still being indexed

Based on Cursor's research showing semantic search improves AI agent performance by 12.5%.

Quick Start

npm install -g semantic-code-mcp

Add to your MCP config:

{
  "mcpServers": {
    "semantic-code-mcp": {
      "command": "semantic-code-mcp",
      "args": ["--workspace", "/path/to/your/project"]
    }
  }
}

That's it. Your AI assistant now has semantic code search.

Features

Multi-Provider Embeddings

Provider	Model	Privacy	Speed
Local (default)	nomic-embed-text-v1.5	100% local	~50ms/chunk
Gemini	gemini-embedding-001	API call	Fast, batched
OpenAI	text-embedding-3-small	API call	Fast
OpenAI-compatible	Any compatible endpoint	Varies	Varies
Vertex AI	Google Cloud models	GCP	Fast

Flexible Vector Storage

• SQLite (default) — zero-config, single-file .smart-coding-cache/embeddings.db
• Milvus — scalable ANN search for large codebases or shared team indexes

Smart Code Chunking

Three modes to match your codebase:

• smart (default) — regex-based, language-aware splitting
• ast — Tree-sitter parsing for precise function/class boundaries
• line — simple fixed-size line chunks

Resource Throttling

CPU capped at 50% during indexing. Your machine stays responsive.

Tools

Tool	Description
a_semantic_search	Find code by meaning. Hybrid semantic + exact match scoring.
b_index_codebase	Trigger manual reindex (normally automatic & incremental).
c_clear_cache	Reset embeddings cache entirely.
d_check_last_version	Look up latest package version from 20+ registries.
e_set_workspace	Switch project at runtime without restart.
f_get_status	Server health: version, index progress, config.

IDE Setup

IDE / App	Guide	${workspaceFolder}
VS Code	Setup	✅
Cursor	Setup	✅
Windsurf	Setup	❌
Claude Desktop	Setup	❌
OpenCode	Setup	❌
Raycast	Setup	❌
Antigravity	Setup	❌

Multi-Project

{
  "mcpServers": {
    "code-frontend": {
      "command": "semantic-code-mcp",
      "args": ["--workspace", "/path/to/frontend"]
    },
    "code-backend": {
      "command": "semantic-code-mcp",
      "args": ["--workspace", "/path/to/backend"]
    }
  }
}

Configuration

All settings via environment variables. Prefix: SMART_CODING_.

Core

Variable	Default	Description
SMART_CODING_VERBOSE	false	Detailed logging
SMART_CODING_MAX_RESULTS	5	Search results returned
SMART_CODING_BATCH_SIZE	100	Files per parallel batch
SMART_CODING_MAX_FILE_SIZE	1048576	Max file size (1MB)
SMART_CODING_CHUNK_SIZE	25	Lines per chunk
SMART_CODING_CHUNKING_MODE	smart	smart / ast / line
SMART_CODING_WATCH_FILES	false	Auto-reindex on changes
SMART_CODING_AUTO_INDEX_DELAY	5000	Background index delay (ms)
SMART_CODING_MAX_CPU_PERCENT	50	CPU cap during indexing

Embedding Provider

Variable	Default	Description
SMART_CODING_EMBEDDING_PROVIDER	local	local / gemini / openai / openai-compatible / vertex
SMART_CODING_EMBEDDING_MODEL	nomic-ai/nomic-embed-text-v1.5	Model name
SMART_CODING_EMBEDDING_DIMENSION	128	MRL dimension (64–768)
SMART_CODING_DEVICE	auto	cpu / webgpu / auto

Gemini

Variable	Default	Description
SMART_CODING_GEMINI_API_KEY	—	API key
SMART_CODING_GEMINI_MODEL	gemini-embedding-001	Model
SMART_CODING_GEMINI_DIMENSIONS	768	Output dimensions
SMART_CODING_GEMINI_BATCH_SIZE	24	Micro-batch size
SMART_CODING_GEMINI_MAX_RETRIES	3	Retry count

OpenAI / Compatible

Variable	Default	Description
SMART_CODING_EMBEDDING_API_KEY	—	API key
SMART_CODING_EMBEDDING_BASE_URL	—	Base URL (compatible only)

Vertex AI

Variable	Default	Description
SMART_CODING_VERTEX_PROJECT	—	GCP project ID
SMART_CODING_VERTEX_LOCATION	us-central1	Region

Vector Store

Variable	Default	Description
SMART_CODING_VECTOR_STORE_PROVIDER	sqlite	sqlite / milvus
SMART_CODING_MILVUS_ADDRESS	—	Milvus endpoint
SMART_CODING_MILVUS_TOKEN	—	Auth token
SMART_CODING_MILVUS_DATABASE	default	Database name
SMART_CODING_MILVUS_COLLECTION	smart_coding_embeddings	Collection

Search Tuning

Variable	Default	Description
SMART_CODING_SEMANTIC_WEIGHT	0.7	Semantic vs exact weight
SMART_CODING_EXACT_MATCH_BOOST	1.5	Exact match multiplier

Example with Gemini + Milvus

{
  "mcpServers": {
    "semantic-code-mcp": {
      "command": "semantic-code-mcp",
      "args": ["--workspace", "/path/to/project"],
      "env": {
        "SMART_CODING_EMBEDDING_PROVIDER": "gemini",
        "SMART_CODING_GEMINI_API_KEY": "YOUR_KEY",
        "SMART_CODING_VECTOR_STORE_PROVIDER": "milvus",
        "SMART_CODING_MILVUS_ADDRESS": "http://localhost:19530"
      }
    }
  }
}

Architecture

semantic-code-mcp/
├── index.js              # MCP server entry point
├── lib/
│   ├── config.js         # Configuration loader
│   ├── cache-factory.js  # SQLite / Milvus provider selection
│   ├── cache.js          # SQLite vector store
│   ├── milvus-cache.js   # Milvus vector store
│   ├── mrl-embedder.js   # Local MRL embedder
│   ├── gemini-embedder.js# Gemini API embedder
│   ├── ast-chunker.js    # Tree-sitter AST chunking
│   ├── tokenizer.js      # Token counting
│   └── utils.js          # Cosine similarity, hashing, smart chunking
├── features/
│   ├── hybrid-search.js  # Semantic + exact match search
│   ├── index-codebase.js # File discovery & incremental indexing
│   ├── clear-cache.js    # Cache reset
│   ├── check-last-version.js  # Package version lookup
│   ├── set-workspace.js  # Runtime workspace switching
│   └── get-status.js     # Server status
└── test/                 # Vitest test suite

How It Works

Your code files
    ↓ glob + .gitignore-aware discovery
Smart/AST chunking
    ↓ language-aware splitting
AI embedding (local or API)
    ↓ vector generation
SQLite or Milvus storage
    ↓ incremental, hash-based updates

Search query
    ↓ embed query → cosine similarity → exact match boost
Top N results with relevance scores

Progressive indexing — search works immediately while indexing continues in the background. Only changed files are re-indexed on subsequent runs.

Privacy

• Local mode: everything runs on your machine. Code never leaves your system.
• API mode: code chunks are sent to the embedding API for vectorization. No telemetry beyond provider API calls.

License

MIT License

Copyright (c) 2025 Omar Haris (original), bitkyc08 (modifications, 2026)

See LICENSE for full text.

Built on smart-coding-mcp by Omar Haris. Extended with multi-provider embeddings, Milvus ANN search, AST chunking, resource throttling, and comprehensive test suite.