# Add to your Claude Code skills
git clone https://github.com/nkapila6/mcp-local-rag
README.md
mcp-local-rag
"primitive" RAG-like web search model context protocol (MCP) server that runs locally. ✨ no APIs ✨
A RAG-based web search and deep research model context protocol (MCP) server that runs entirely locally. Features multi-engine research across 9+ search backends with semantic similarity ranking, and requires no API keys.
%%{init: {'theme': 'base'}}%%
flowchart TD
A[User] -->|1.Submits LLM Query| B[Language Model]
B -->|2.Sends Query| C[mcp-local-rag Tool]
subgraph mcp-local-rag Processing
C -->|Search DuckDuckGo| D[Fetch 10 search results]
D -->|Fetch Embeddings| E[Embeddings from Google's MediaPipe Text Embedder]
E -->|Compute Similarity| F[Rank Entries Against Query]
F -->|Select top k results| G[Context Extraction from URL]
end
G -->|Returns Markdown from HTML content| B
B -->|3.Generated response with context| H[Final LLM Output]
H -->|5.Present result to user| A
classDef default stroke:#333,stroke-width:2px;
classDef process stroke:#333,stroke-width:2px;
classDef input stroke:#333,stroke-width:2px;
classDef output stroke:#333,stroke-width:2px;
class A input;
class B,C process;
class G output;
This repository includes Agent Skills that teach Claude how to effectively use the mcp-local-rag tools for intelligent web searches and deep research. Skills are folders of instructions that Claude loads dynamically to improve performance on specialized tasks.
Available Skills
local-rag-search - Teaches Claude best practices for:
Smart tool selection: Choosing between quick searches or comprehensive deep research
Multi-engine research: Using multiple search backends for diverse perspectives
Effective query formulation: Writing natural language queries that yield better results
Parameter tuning: Adjusting num_results, top_k, and backend selection for different use cases
Privacy-aware searching: Defaulting to privacy-focused engines while allowing comprehensive searches when needed
Deep Research Use Cases
The skill enables comprehensive topic research using multiple search terms and engines. It's particularly useful for technical deep dives that leverage Google's documentation coverage, multi-perspective analysis that compares information across different search engines, privacy-focused research using DuckDuckGo or Brave, and factual verification by cross-referencing Wikipedia and other authoritative sources.
Using the Skills
In Claude Desktop:
Go to Settings → Skills
Click Add Skill → Add from folder
Select skills/local-rag-search/
In conversations:
Once loaded, simply ask Claude to search for information and it will automatically apply the skill's best practices. Try queries like:
"Do deep research on recent quantum computing developments"
"Search multiple sources for sustainable energy solutions"
"Find comprehensive technical documentation about Kubernetes optimization"
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
⭐AI-driven public opinion & trend monitor with multi-platform aggregation, RSS, and smart alerts.🎯 告别信息过载，你的 AI 舆情监控助手与热点筛选工具！聚合多平台热点 + RSS 订阅，支持关键词精准筛选。AI 智能筛选新闻 + AI 翻译 + AI 分析简报直推手机，也支持接入 MCP 架构，赋能 AI 自然语言对话分析、情感洞察与趋势预测等。支持 Docker ，数据本地/云端自持。集成微信/飞书/钉钉/Telegram/邮件/ntfy/bark/slack 等渠道智能推送。
