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

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Smart Coding MCP

npm version npm downloads License: MIT Node.js

An extensible Model Context Protocol (MCP) server that provides intelligent semantic code search for AI assistants. Built with local AI models using Matryoshka Representation Learning (MRL) for flexible embedding dimensions (64-768d.

Available Tools

Tool Description Example
semantic_search Find code by meaning, not just keywords "Where do we validate user input?"
index_codebase Manually trigger reindexing Use after major refactoring or branch switches
clear_cache Reset the embeddings cache Useful when cache becomes corrupted
d_check_last_version Get latest version of any package (20 ecosystems) "express", "npm:react", "pip:requests"
e_set_workspace Change project path at runtime Switch to different project without restart
f_get_status Get server info: version, index status, config Check indexing progress, model info, cache size

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. Zero-configuration required—just run it and it works.

Example

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

Performance & Resource Management

Progressive Indexing

  • Search works immediately, even while indexing continues (like video buffering)
  • Incremental saves every 5 batches - no data loss if interrupted
  • Real-time indexing status shown when searching during indexing

Resource Throttling

  • CPU usage limited to 50% by default (configurable)
  • Your laptop stays responsive during indexing
  • Configurable delays between batches
  • Worker thread limits respect system resources

SQLite Cache

  • 5-10x faster than JSON for large codebases
  • Write-Ahead Logging (WAL) for better concurrency
  • Binary blob storage for smaller cache size
  • Automatic migration from JSON

Optimized Defaults

  • 128d embeddings by default (2x faster than 256d, minimal quality loss)
  • Smart batch sizing based on project size
  • Parallel processing with auto-tuned worker threads

Installation

Install globally via npm:

npm install -g smart-coding-mcp

Or run directly without installation using npx:

npx smart-coding-mcp

To update to the latest version:

npm update -g smart-coding-mcp

Configuration

IDE Integration

Detailed setup instructions for your preferred environment:

IDE / App Setup Guide Supported Variables
VS Code View Guide ${workspaceFolder}
Cursor View Guide ${workspaceFolder}
Windsurf View Guide Absolute paths only
Claude Desktop View Guide Absolute paths only
Raycast View Guide Absolute paths only
Antigravity View Guide Absolute paths only

Common Configuration Paths

IDE OS Config Path
Claude Desktop macOS ~/Library/Application Support/Claude/claude_desktop_config.json
Claude Desktop Windows %APPDATA%\Claude\claude_desktop_config.json
Windsurf macOS ~/.codeium/windsurf/mcp_config.json
Windsurf Windows %USERPROFILE%\.codeium\windsurf\mcp_config.json

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

Option 1: Zero-Config (Recommended)

The simplest way to use the server is via npx. No arguments needed. The server will automatically detect your project root from the IDE's handshake protocol.

{
  "mcpServers": {
    "smart-coding-mcp": {
      "command": "npx",
      "args": ["-y", "smart-coding-mcp"]
    }
  }
}

How it works:

  1. Your IDE starts the server.
  2. The server waits for the first initialize message.
  3. It detects the rootUri or workspaceFolders sent by the IDE.
  4. It indexes that folder automatically.

Client Compatibility Table:

Client Zero-Config Support Notes
VS Code ✅ Yes Best experience.
Cursor ✅ Yes Fully supported.
Claude Desktop ❌ No Does not send workspace context. Use Option 2.
Antigravity ⚠️ Partial Depends on version. If automatic detection fails, use Option 2.

Option 2: Explicit Configuration (Robust Fallback)

If Zero-Config doesn't work (e.g., folder not created), or if you are using a client that doesn't send workspace context (like Claude Desktop or some Antigravity versions), use explicit arguments:

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

Tip

Use Proper Paths: Windows users should use forward slashes / (e.g., C:/Projects/MyCode) to avoid JSON escaping issues.

Helper for Antigravity Users

If manual configuration feels too complex, we built a simple auto-setup command.

  1. Open your project in the terminal.
  2. Run this single command:
npx smart-coding-mcp --configure

This will automatically find your Antigravity config file and update it to point to your current folder. Then, simply Reload Window to finish.

Feature: Smart Shutdown

The server includes a "zombie process" protection mechanism. It monitors the standard input connection from the IDE; if the IDE closes or crashes, the server automatically shuts down to prevent resource exhaustion (memory leaks).

Option 3: Cross-Project Search (Advanced)

Note: You do NOT need this if you just want to work on different projects in different windows. Option 1 already handles that automatically by launching a separate instance for each window.

Use this option ONLY if you need to search code from another project while working in your current one (e.g., searching your backend API repo while working in your frontend repo).

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

Compatibility Note: Dynamic Variables

⚠️ Warning: Most MCP clients (including Antigravity and Claude Desktop) do NOT support ${workspaceFolder} variable expansion. The server will exit with an error if the variable is not expanded.

Client Supports ${workspaceFolder}
VS Code Yes
Cursor (Cascade) Yes
Antigravity No ❌
Claude Desktop No ❌

Troubleshooting & CLI

To see all available options and environment variables, you can run the server with the --help flag:

npx smart-coding-mcp --help

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 25 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 nomic-ai/nomic-embed-text-v1.5 AI embedding model to use
SMART_CODING_EMBEDDING_DIMENSION number 128 MRL dimension (64, 128, 256, 512, 768)
SMART_CODING_DEVICE string cpu Inference device (cpu, webgpu, auto)
SMART_CODING_CHUNKING_MODE string smart Code chunking (smart, ast, line)
SMART_CODING_WORKER_THREADS string auto Worker threads (auto or 1-32)
SMART_CODING_MAX_CPU_PERCENT number 50 Max CPU usage during indexing (10-100%)
SMART_CODING_BATCH_DELAY number 100 Delay between batches in ms (0-5000)
SMART_CODING_MAX_WORKERS string auto Override max worker threads limit

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.

How It Works

flowchart TB
    subgraph IDE["IDE / AI Assistant"]
        Agent["AI Agent<br/>(Claude, GPT, Gemini)"]
    end

    subgraph MCP["Smart Coding MCP Server"]
        direction TB
        Protocol["Model Context Protocol<br/>JSON-RPC over stdio"]
        Tools["MCP Tools<br/>semantic_search | index_codebase | set_workspace | get_status"]

        subgraph Indexing["Indexing Pipeline"]
            Discovery["File Discovery<br/>glob patterns + smart ignore"]
            Chunking["Code Chunking<br/>Smart (regex) / AST (Tree-sitter)"]
            Embedding["AI Embedding<br/>transformers.js + ONNX Runtime"]
        end

        subgraph AI["AI Model"]
            Model["nomic-embed-text-v1.5<br/>Matryoshka Representation Learning"]
            Dimensions["Flexible Dimensions<br/>64 | 128 | 256 | 512 | 768"]
            Normalize["Layer Norm → Slice → L2 Normalize"]
        end

        subgraph Search["Search"]
            QueryEmbed["Query → Vector"]
            Cosine["Cosine Similarity"]
            Hybrid["Hybrid Search<br/>Semantic + Exact Match Boost"]
        end
    end

    subgraph Storage["Cache"]
        Vectors["SQLite Database<br/>embeddings.db (WAL mode)"]
        Hashes["File Hashes<br/>Incremental updates"]
        Progressive["Progressive Indexing<br/>Search works during indexing"]
    end

    Agent <-->|"MCP Protocol"| Protocol
    Protocol --> Tools

    Tools --> Discovery
    Discovery --> Chunking
    Chunking --> Embedding
    Embedding --> Model
    Model --> Dimensions
    Dimensions --> Normalize
    Normalize --> Vectors

    Tools --> QueryEmbed
    QueryEmbed --> Model
    Cosine --> Hybrid
    Vectors --> Cosine
    Hybrid --> Agent
Loading

Tech Stack

Component Technology
Protocol Model Context Protocol (JSON-RPC)
AI Model nomic-embed-text-v1.5 (MRL)
Inference transformers.js + ONNX Runtime
Chunking Smart regex / Tree-sitter AST
Search Cosine similarity + exact match boost

Supported Languages

  • Web: JavaScript, TypeScript, HTML, CSS, PHP
  • System: C, C++, Rust, Go, Swift
  • Data/Config: JSON, YAML, XML, SQL, CSV
  • Game Dev: UEFN Verse (.verse), Lua, C#
  • Scripting: Python, Ruby, Shell
  • Docs: Markdown, Text

Search Flow

Query → Vector embedding → Cosine similarity → Ranked results

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: nomic-embed-text-v1.5 via transformers.js v3

  • Matryoshka Representation Learning (MRL) for flexible dimensions
  • Configurable output: 64, 128, 256, 512, or 768 dimensions
  • Longer context (8192 tokens vs 256 for MiniLM)
  • Better code understanding through specialized training
  • WebGPU support for up to 100x faster inference (when available)

Legacy Model: all-MiniLM-L6-v2 (fallback)

  • Fast inference, small footprint (~100MB)
  • Fixed 384-dimensional output

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.

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

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