semanticscout 3.1.4

A language-aware semantic code search MCP server with intelligent filtering and 9.3x better dependency analysis

SemanticScout 🔍

Please note: this is just an idea project to try and build something for use in non Augment Code world

I have yet to refactor lots of slop, and implement a bunch of key changes

Language-aware semantic code search for AI agents withdependency analysis

SemanticScout is a Model Context Protocol (MCP) server that provides intelligent code search for AI agents. It combines semantic search with language-aware analysis to understand code relationships, dependencies, and architecture.

✨ Key Features

• 🎯 Language-Aware Analysis - Automatic language detection with specialized dependency analysis (Rust, C#, Python, etc.)
• 🔍 Semantic Code Search - Natural language queries with 100% accuracy and intelligent context expansion
• 🚫 Smart Test Filtering - Automatically excludes test files (0% test pollution) with multi-strategy detection
• 🗂️ Git Integration - Smart filtering of untracked files and incremental indexing (5-10x faster updates)
• 🧠 Hybrid Retrieval - Combines semantic, symbol, and dependency-based search with AST parsing
• ⚡ High Performance - Local embeddings (sentence-transformers), <100ms queries, <2s per file indexing
• 🌐 Multi-Language - TypeScript, JavaScript, Python, Java, C#, Go, Rust, Ruby, PHP, C, C++
• 🤖 MCP Ready - Works with Claude Desktop and other MCP clients out of the box

🚀 Quick Start

Get started in under 2 minutes with zero configuration required!

Prerequisites

• uv - Install uv
• Claude Desktop - Install Claude Desktop

Setup

1. Configure Claude Desktop - Add to your MCP configuration file:

Windows: %APPDATA%\Claude\claude_desktop_config.json Mac: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "semanticscout": {
      "command": "uvx",
      "args": ["--python", "3.12", "semanticscout@latest"]
    }
  }
}

2. Restart Claude Desktop - SemanticScout will be automatically downloaded and ready to use!

✨ What you get:

• Language-aware analysis with automatic project detection
• Fast local embeddings (sentence-transformers, no Ollama needed)
• Smart test file filtering and git integration
• All data stored in ~/semanticscout/

Note: Use Python 3.12 for best compatibility. Some dependencies don't yet support Python 3.13.

📖 Usage

Once configured, use natural language to interact with SemanticScout through Claude:

Example Conversations

Index a codebase:

You: "Index my codebase at /workspace"
Claude: [Calls index_codebase tool and shows indexing progress]

Search for code:

You: "Find the authentication logic"
Claude: [Calls search_code tool and shows relevant code snippets]

Advanced queries:

You: "Show me dependency injection configuration"
Claude: [Automatically detects architectural query and expands coverage]

Available Tools

Tool	Description	Key Parameters
index_codebase	Index a codebase with language-aware analysis	path, incremental
search_code	Search with natural language + smart filtering	query, collection_name, exclude_test_files
find_symbol	Find symbols with language-aware lookup	symbol_name, collection_name
trace_dependencies	Trace dependency chains	file_path, collection_name, depth
list_collections	List all indexed codebases	None

Advanced Features

• Incremental Indexing: Use incremental=True for 5-10x faster updates on existing codebases
• Test Filtering: Set exclude_test_files=False to include test files in search results
• Coverage Modes: Use coverage_mode for different result depths (focused/balanced/comprehensive/exhaustive)
• Real-time Updates: Process file change events from editors automatically

🔧 Configuration

Default Setup (Recommended)

The default configuration works great for most users - no additional setup needed!

Custom Embedding Models

To use a different sentence-transformers model:

{
  "mcpServers": {
    "semanticscout": {
      "command": "uvx",
      "args": ["--python", "3.12", "semanticscout@latest"],
      "env": {
        "SEMANTICSCOUT_CONFIG_JSON": "{\"embedding\":{\"provider\":\"sentence-transformers\",\"model\":\"all-mpnet-base-v2\"}}"
      }
    }
  }
}

Ollama (Optional - GPU Acceleration)

For GPU acceleration with Ollama:

# Start Ollama and pull model
ollama serve
ollama pull nomic-embed-text

{
  "mcpServers": {
    "semanticscout": {
      "command": "uvx",
      "args": ["--python", "3.12", "semanticscout@latest"],
      "env": {
        "OLLAMA_BASE_URL": "http://localhost:11434",
        "OLLAMA_MODEL": "nomic-embed-text",
        "SEMANTICSCOUT_CONFIG_JSON": "{\"embedding\":{\"provider\":\"ollama\"}}"
      }
    }
  }
}

🐛 Troubleshooting

Common Issues

Python Version Error: Use Python 3.12 for best compatibility (some dependencies don't support 3.13 yet)

Ollama Not Available: The default uses sentence-transformers (no Ollama needed). Only configure Ollama if you want GPU acceleration.

Rate Limits: Adjust limits with environment variables:

"env": {
  "MAX_INDEXING_REQUESTS_PER_HOUR": "20",
  "MAX_SEARCH_REQUESTS_PER_MINUTE": "200"
}

📚 Documentation

• API Reference - Complete tool documentation
• User Guide - Examples and best practices
• Configuration - Advanced configuration options
• Performance Tuning - Optimization guide

🏗️ Architecture

SemanticScout combines multiple technologies for intelligent code search:

• Language Detection → AST Parsing (tree-sitter) → Symbol Extraction
• Semantic Chunking → Embeddings (sentence-transformers/Ollama) → Vector Storage (ChromaDB)
• Dependency Analysis → Graph Storage (NetworkX) → Symbol Tables (SQLite)
• Hybrid Search → Context Expansion → Smart Filtering

🤝 Contributing

Contributions welcome! See our contributing guide for details.

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

📄 License

MIT License - see LICENSE for details.

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