# Local RAG Search - Agent Skill
An Agent Skill that teaches Claude how to effectively use the [mcp-local-rag](https://github.com/nkapila6/mcp-local-rag) MCP server for intelligent web searches with semantic similarity ranking.
## What This Skill Does
This skill enables agents to:
- **Choose the right search tool** based on the task (DuckDuckGo, Google, or multi-engine deep research)
- **Formulate effective queries** using natural language
- **Tune parameters** for different use cases (quick answers vs comprehensive research)
- **Perform deep research** across multiple search engines and topics
- **Respect privacy** by defaulting to DuckDuckGo
## Prerequisites
This skill requires the [mcp-local-rag MCP server](https://github.com/nkapila6/mcp-local-rag) to be installed and configured in your MCP client.
### Install mcp-local-rag
Add to your MCP configuration:
```json
{
"mcpServers": {
"mcp-local-rag": {
"command": "uvx",
"args": [
"--python=3.10",
"--from",
"git+https://github.com/nkapila6/mcp-local-rag",
"mcp-local-rag"
]
}
}
}
```
Or use Docker:
```json
{
"mcpServers": {
"mcp-local-rag": {
"command": "docker",
"args": [
"run", "--rm", "-i", "--init",
"-e", "DOCKER_CONTAINER=true",
"ghcr.io/nkapila6/mcp-local-rag:v1.0.2"
]
}
}
}
```
## Installation
### Claude Desktop
1. Navigate to **Settings** → **Skills**
2. Click **Add Skill** → **Add from folder**
3. Select this skill folder (`local-rag-search/`)
## Usage
Once both the MCP server and skill are loaded, simply ask Claude to search for information:
- "Search the web for the latest Python 3.13 features"
- "Do deep research on sustainable energy solutions"
- "Find technical documentation about Docker optimization"
Claude will automatically apply the skill's best practices to use the appropriate tools effectively.
## Features
- ✅ **Smart tool selection** - Automatically chooses DuckDuckGo, Google, or deep research based on query
- ✅ **Privacy-first** - Defaults to DuckDuckGo for general searches
- ✅ **Multi-engine research** - Supports 9+ search backends for comprehensive coverage
- ✅ **Semantic ranking** - Uses RAG-like similarity scoring for most relevant results
- ✅ **No external APIs** - All processing runs locally with embedded models
## Supported Search Backends
- DuckDuckGo (privacy-focused)
- Google (comprehensive)
- Bing, Brave, Yahoo, Yandex
- Wikipedia (factual/encyclopedia)
- Mojeek, Grokipedia
## License
MIT - Same as the parent mcp-local-rag project
