What is the key difference between MCP Local Rag and Wet MCP?

MCP Local Rag belongs to the Search & Data Extraction category with local stdio execution, while Wet MCP belongs to the Search & Data Extraction category with local stdio execution.

How do I install MCP Local Rag or Wet MCP in Claude Desktop or Cursor?

Copy the mcpServers JSON configuration snippet provided on this page into your claude_desktop_config.json or ~/.cursor/mcp.json file and restart your AI client.

Are MCP Local Rag and Wet MCP free or paid?

MCP Local Rag operates under a Free / Open Source model (No auth required). Wet MCP operates under a Free / Open Source model (No auth required).

Which MCP server has more tools and community activity?

MCP Local Rag lists 5 tools with 2 views and 134 GitHub stars. Wet MCP lists 6 tools with 1 views and 18 GitHub stars.

Can I use both MCP Local Rag and Wet MCP together in the same MCP host?

Yes! MCP hosts like Claude Desktop, Cursor, Windsurf, Cline, and VS Code support configuring multiple servers under the mcpServers object simultaneously.

Side-by-Side Model Context Protocol Comparison

MCP Local Rag vs Wet MCP

In-depth architectural comparison of the MCP Local Rag and Wet MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.

At a Glance & Executive Verdict

MCP Local Rag

Search & Data Extraction · Local stdio

Quality: 69/100 (Great) | Auth: No auth required

Wet MCP

Search & Data Extraction · Local stdio

Quality: 59/100 (Good) | Auth: No auth required

Verdict Summary: Choose MCP Local Rag if you need specialized Search & Data Extraction tools running via a local process. Choose Wet MCP if your workspace requires Search & Data Extraction integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.

Which MCP Server Should You Choose?

Choose MCP Local Rag when:

You need dedicated capabilities in the Search & Data Extraction domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Primary tools included: rag_search_ddgs, rag_search_google, deep_research.

Explore MCP Local Rag Details

Choose Wet MCP when:

You need dedicated capabilities in the Search & Data Extraction domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
You have access to required keys: WET_MCP_CONFIG.
Primary tools included: search, extract, media.

Explore Wet MCP Details

Feature & Specification Comparison

Specification	MCP Local Rag nkapila6 Search & Data Extraction	Wet MCP n24q02m Search & Data Extraction
Summary	"primitive" RAG-like web search model context protocol (MCP) server that runs locally. No APIs needed.	Web search (embedded SearXNG), content extraction, and library docs indexing with hybrid search (FTS5 + semantic). Built-in Qwen3 embedding, no API keys required.
Category & Scope	Search & Data Extraction

Tools & Capabilities Breakdown

MCP Local Rag Tools (5)

rag_search_ddgs

Search the web for a given query using DuckDuckGo. Returns context to the LLM with RAG-like similarity scoring to prioritize the most relevant results. This tool fetches web search results, scores them by semantic similarity to the query using text embeddings, and returns the top-ranked content as markdown text.

rag_search_google

Search on Google for a given query using ddgs. Give back context to the LLM with a RAG-like similarity sort.

deep_research

Perform deep research across multiple search terms using specified search backends. This tool aggregates results from multiple searches across chosen engines, scores them by relevance, and returns the most relevant content with duplicates removed. Perfect for comprehensive research on a topic. Available backends: bing, brave, duckduckgo, google, grokipedia, mojeek, yandex, yahoo, wikipedia USAGE GUIDANCE FOR LLM: 1. Ask the user which backend(s) they prefer, OR 2. Choose appropriate backend(s) based on context: - ["duckduckgo"] - Privacy-focused, general search - ["google"] - Comprehensive results, best for technical queries - ["duckduckgo", "google"] - Maximum coverage (default) - ["wikipedia"] - Factual/encyclopedia content - ["bing", "google"] - Balanced commercial engines - Multiple backends for broader research coverage 3. For specific use cases, consider: - deep_research_google() - shortcut for Google-only - deep_research_ddgs() - shortcut for DuckDuckGo-only

Ready-to-Paste Client Configurations

Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).

MCP Local Rag Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "nkapila6-mcp-local-rag": {
      "command": "uvx",
      "args": [
        "--python=3.10",
        "--from",
        "git+https://github.com/nkapila6/mcp-local-rag",
        "mcp-local-rag"
      ]
    }
  }
}

Wet MCP Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "n24q02m-wet-mcp": {
      "command": "uvx",
      "args": [
        "invocation"
      ],
      "env": {
        "WET_MCP_CONFIG": "YOUR_WET_MCP_CONFIG_HERE"
      }
    }
  }
}

Frequently Asked Questions

MCP Local Rag is categorized under Search & Data Extraction and uses a local stdio subprocess. In contrast, Wet MCP belongs to Search & Data Extraction using local stdio subprocess. Select MCP Local Rag when you need capabilities focused on search & data extraction and Wet MCP when you require tools for search & data extraction.

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