Smart Coding MCP

An extensible Model Context Protocol (MCP) server that provides intelligent semantic code search for AI assistants. Built with local AI models, inspired by Cursor's semantic search research.

What This Does

AI coding assistants work better when they can find relevant code quickly. Traditional keyword search falls short - if you ask "where do we handle authentication?" but your code uses "login" and "session", keyword search misses it.

This MCP server solves that by indexing your codebase with AI embeddings. Your AI assistant can search by meaning instead of exact keywords, finding relevant code even when the terminology differs.

Why Use This

Better Code Understanding

• Search finds code by concept, not just matching words
• Works with typos and variations in terminology
• Natural language queries like "where do we validate user input?"

Performance

• Pre-indexed embeddings are faster than scanning files at runtime
• Smart project detection skips dependencies automatically (node_modules, vendor, etc.)
• Incremental updates - only re-processes changed files

Privacy

• Everything runs locally on your machine
• Your code never leaves your system
• No API calls to external services

Installation

Install globally via npm:

npm install -g smart-coding-mcp

To update to the latest version:

npm update -g smart-coding-mcp

Configuration

Add to your MCP configuration file. The location depends on your IDE and OS:

| IDE | OS | Config Path | | -------------------- | ------- | ----------------------------------------------------------------- | | Claude Desktop | macOS | ~/Library/Application Support/Claude/claude_desktop_config.json | | Claude Desktop | Windows | %APPDATA%\Claude\claude_desktop_config.json | | Cascade (Cursor) | All | Configured via UI Settings > Features > MCP | | Antigravity | macOS | ~/.gemini/antigravity/mcp_config.json | | Antigravity | Windows | %USERPROFILE%\.gemini\antigravity\mcp_config.json |

Add the server configuration to the mcpServers object in your config file:

Option 1: Specific Project (Recommended)

{
  "mcpServers": {
    "smart-coding-mcp": {
      "command": "smart-coding-mcp",
      "args": ["--workspace", "/absolute/path/to/your/project"]
    }
  }
}

Option 2: Multi-Project Support

{
  "mcpServers": {
    "smart-coding-mcp-project-a": {
      "command": "smart-coding-mcp",
      "args": ["--workspace", "/path/to/project-a"]
    },
    "smart-coding-mcp-project-b": {
      "command": "smart-coding-mcp",
      "args": ["--workspace", "/path/to/project-b"]
    }
  }
}

Environment Variables

Override configuration settings via environment variables in your MCP config:

| Variable | Type | Default | Description | | -------------------------------- | ------- | ------------------------- | ------------------------------------- | | SMART_CODING_VERBOSE | boolean | false | Enable detailed logging | | SMART_CODING_BATCH_SIZE | number | 100 | Files to process in parallel | | SMART_CODING_MAX_FILE_SIZE | number | 1048576 | Max file size in bytes (1MB) | | SMART_CODING_CHUNK_SIZE | number | 15 | Lines of code per chunk | | SMART_CODING_MAX_RESULTS | number | 5 | Max search results | | SMART_CODING_SMART_INDEXING | boolean | true | Enable smart project detection | | SMART_CODING_WATCH_FILES | boolean | false | Enable file watching for auto-reindex | | SMART_CODING_SEMANTIC_WEIGHT | number | 0.7 | Weight for semantic similarity (0-1) | | SMART_CODING_EXACT_MATCH_BOOST | number | 1.5 | Boost for exact text matches | | SMART_CODING_EMBEDDING_MODEL | string | Xenova/all-MiniLM-L6-v2 | AI embedding model to use |

Example with environment variables:

{
  "mcpServers": {
    "smart-coding-mcp": {
      "command": "smart-coding-mcp",
      "args": ["--workspace", "/path/to/project"],
      "env": {
        "SMART_CODING_VERBOSE": "true",
        "SMART_CODING_BATCH_SIZE": "200",
        "SMART_CODING_MAX_FILE_SIZE": "2097152"
      }
    }
  }
}

Note: The server starts instantly and indexes in the background, so your IDE won't be blocked waiting for indexing to complete.

Available Tools

semantic_search - Find code by meaning

Query: "Where do we validate user input?"
Returns: Relevant validation code with file paths and line numbers

index_codebase - Manually trigger reindexing

Use after major refactoring or branch switches

clear_cache - Reset the embeddings cache

Useful when cache becomes corrupted or outdated

How It Works

The server indexes your code in four steps:

1. Discovery: Scans your project for source files
2. Chunking: Breaks code into meaningful pieces (respecting function boundaries)
3. Embedding: Converts each chunk to a vector using a local AI model
4. Storage: Saves embeddings to .smart-coding-cache/ for fast startup

When you search, your query is converted to the same vector format and compared against all code chunks using cosine similarity. The most relevant matches are returned.

Examples

Natural language search:

Query: "How do we handle cache persistence?"

Result:

// lib/cache.js (Relevance: 38.2%)
async save() {
  await fs.writeFile(cacheFile, JSON.stringify(this.vectorStore));
  await fs.writeFile(hashFile, JSON.stringify(this.fileHashes));
}

Typo tolerance:

Query: "embeding modle initializashun"

Still finds embedding model initialization code despite multiple typos.

Conceptual search:

Query: "error handling and exceptions"

Finds all try/catch blocks and error handling patterns.

Privacy

• AI model runs entirely on your machine
• No network requests to external services
• No telemetry or analytics
• Cache stored locally in .smart-coding-cache/

Technical Details

Embedding Model: all-MiniLM-L6-v2 via transformers.js

• Fast inference (CPU-friendly)
• Small model size (~100MB)
• Good accuracy for code search

Vector Similarity: Cosine similarity

• Efficient comparison of embeddings
• Normalized vectors for consistent scoring

Hybrid Scoring: Combines semantic similarity with exact text matching

• Semantic weight: 0.7 (configurable)
• Exact match boost: 1.5x (configurable)

Research Background

This project builds on research from Cursor showing that semantic search improves AI coding agent performance by 12.5% on average across question-answering tasks. The key insight is that AI assistants benefit more from relevant context than from large amounts of context.

See: https://cursor.com/blog/semsearch

License

MIT License

Copyright (c) 2025 Omar Haris

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.