A side-by-side look at the TinySearch and MCP Local Rag MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose TinySearch for search & data extraction work as a local process; choose MCP Local Rag for search & data extraction as a local process. You can also run both side by side in one mcpServers block.
rag_search_ddgs, rag_search_google, deep_research.Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Search & Data Extraction | Search & Data Extraction |
| Quality signal | 56/100 · Good | 67/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | No auth required | No auth required |
| Pricing | Free | Free |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 230 | 134 |
| Directory activity | 2 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | Not listed | 5 |
| Full listing | View TinySearch | View MCP Local Rag |
No tool names declared yet. Check the project README from the listing page.
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.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.
{
"mcpServers": {
"marcellm01-tinysearch": {
"command": "uvx",
"args": [
"--from",
"tinysuite-search[server]",
"tinysearch"
]
}
}
}
{
"mcpServers": {
"nkapila6-mcp-local-rag": {
"command": "uvx",
"args": [
"--python=3.10",
"--from",
"git+https://github.com/nkapila6/mcp-local-rag",
"mcp-local-rag"
]
}
}
}