MCP Local Rag vs Rag
A side-by-side look at the MCP Local Rag and Rag MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose MCP Local Rag for search & data extraction work as a local process; choose Rag for search & data extraction. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose MCP Local Rag when
- You need dedicated capabilities in the Search & Data Extraction domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
rag_search_ddgs, rag_search_google, deep_research.
Choose Rag when
- You need dedicated capabilities in the Search & Data Extraction domain.
- You want tools like
list_knowledge_bases, search_documents, get_document_chunk.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Search & Data Extraction | Search & Data Extraction |
| Quality signal | 64/100 · Good | 41/100 · Fair |
| Install command | Confirmed | Not confirmed yet |
| Transport | Local subprocess (stdio) | — |
| Auth | No auth required | Not declared |
| Pricing | Free | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 135 | 0 |
| Directory activity | 3 views 0 installs 0 upvotes | 0 views 0 installs 0 upvotes |
| Tools | 5 | 7 |
| Full listing | View MCP Local Rag | View Rag |
Tools & capabilities
MCP Local Rag tools 5
rag_search_ddgsSearch 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_googleSearch on Google for a given query using ddgs. Give back context to the LLM with a RAG-like similarity sort.deep_researchPerform 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-onlydeep_research_googlePerform deep research across multiple search terms using ONLY Google. Aggregates results from multiple Google searches, scores them by relevance, and returns the most relevant content with duplicates removed.deep_research_ddgsPerform deep research across multiple search terms using ONLY DuckDuckGo. Aggregates results from multiple DuckDuckGo searches, scores them by relevance, and returns the most relevant content with duplicates removed.
Rag tools 7
list_knowledge_basesList all available KBs with status (file/chunk counts, indexing status)search_documentsHybrid BM25 + vector search with RRF. Supports `search_mode`: `auto`, `bm25`, `vectorget_document_chunkRetrieve full content of a specific chunk by IDstart_reindexQueue an indexing request. Scope merge, force/deferred flags, orchestrator auto-spawncancel_reindexRemove a pending (not-yet-started) queue entryget_index_infoIndex metadata, queue state (status/position/deferred/last_error), contextualization healthcheck_changesDry-run filesystem scan. Lightweight, no API calls
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
MCP Local Rag
{
"mcpServers": {
"nkapila6-mcp-local-rag": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/nkapila6/mcpsurfr",
"mcpsurfr"
]
}
}
}
Rag
No confirmed install command for Rag yet — follow the setup steps linked from its listing page rather than a guessed package name.
Frequently asked questions
MCP Local Rag is categorized under Search & Data Extraction and uses a local stdio subprocess. Rag belongs to Search & Data Extraction and uses an install path we have not confirmed yet. Pick MCP Local Rag for search & data extraction work and Rag for search & data extraction.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client. Rag has no confirmed install command yet — follow its own setup instructions.
MCP Local Rag is listed as free. Auth: no auth required. Rag has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add nkapila6-mcp-local-rag and rag as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, MCP Local Rag has a quality signal of 64/100 (Good) with 3 views, 0 installs and 135 GitHub stars. Rag has 41/100 (Fair) with 0 views, 0 installs and 0 GitHub stars.