search-engine
Uses the Algolia HN search API to return Hacker News results with points and comment counts.
Searches arXiv's Atom API for academic papers, returning papers, authors, and links.
Queries DuckDuckGo's instant-answer API and HTML endpoint to return web search results and related topics.
Searches Wikipedia's OpenSearch and summary API to return article results and summaries.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@search-enginesearch for recent papers on retrieval-augmented generation"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP SearchEngine
An MCP server that searches & scrapes multiple sources, then renders an interactive picker UI (a FastMCP App) so you can click which sources you want to delve into. Built on the FastMCP 4 prerelease and the MCP Apps extension.
How it works
LLM ──search(query)──► server ──► queries web, DuckDuckGo, Wikipedia,
│ arXiv & Hacker News concurrently
│ and returns normalized results
│
│ ┌──── ui://search-engine/picker.html ────┐
│ │ interactive card grid, source filters, │
│◄────── rendered app ───┤ multi-select, "Delve into selected" │
│ └──────────────┬─────────────────────────┘
│ │ callServerTool('delve_sources')
LLM ◄── delve_sources(picks) ◄──────────┘
scrapes each picked URL and
returns full readable contentThe flow is symmetrical: the LLM gets the same JSON the UI renders, so it can
also pick sources programmatically and call delve_sources directly — the UI
is additive, not a gate.
Related MCP server: evo-scry
Sources
Source | Backend | Notes |
| DuckDuckGo instant-answer API | abstract + related topics |
| DuckDuckGo HTML endpoint | general web results, no key |
| Wikipedia OpenSearch / summary API | |
| arXiv Atom API | papers, authors, links |
| Algolia HN search API | points + comment counts |
| Reddit public JSON search | no API key, own UA |
| Nitter HTML instances | keyword search; nitter mirror configurable via |
| Perplexity Chat API | opt-in, needs |
A failing source never sinks the search — it comes back as an error card in the UI.
Tools
search(query, sources?, max_per_source?)— search all sources and render the picker UI. Returns JSON{ mode: "pick", results: [...] }plus a text summary.delve_sources(picks, max_chars?)— scrape the picked URLs concurrently and return full extracted text per page.picksitems need at least aurl.
The interactive UI
search_engine/ui/picker.html is served as a ui:// resource
(text/html;profile=mcp-app) and rendered in a sandboxed iframe by hosts that
support MCP Apps (Claude, ChatGPT, VS Code, Goose, …). It uses the
@modelcontextprotocol/ext-apps SDK:
app.ontoolresult— receives the search payload pushed by the hostapp.callServerTool({ name: "delve_sources", … })— delves into picked sources
Click cards to multi-select, double-click to delve into a single source, filter by source chip, then press Delve into selected. The scraped pages are shown in the UI and returned to the conversation.
Install & run
python -m venv .venv
.\.venv\Scripts\python -m pip install -e .The pyproject.toml pins the FastMCP 4 prerelease (fastmcp==4.0.0b1 +
fastmcp-slim==4.0.0b1 constraint, per the v4 upgrade guide).
Run over stdio (what most MCP clients use):
.\.venv\Scripts\python -m search_engineor test it in-process:
.\.venv\Scripts\python test_client.pyThere is also a Playwright-driven UI harness (test_ui_harness.py) that renders
the real picker HTML in Chromium with the SDK stubbed, clicks through select →
filter → delve, and screenshots each stage. Run it with
.\.venv\Scripts\python test_ui_harness.py (requires playwright install chromium).
Privacy & proxying
All network egress (search + delve_sources scraping) goes through a single
privacy layer (search_engine/net.py):
No local persistence, ever. There is no browser: no cache, history or cookies on disk. Every request gets a fresh ephemeral client with its own empty cookie jar that is discarded afterwards.
Proxy via env —
SEARCH_PROXY_URL(falling back toHTTPS_PROXY, thenALL_PROXY). Supportshttp://,socks5://andsocks5h://(thehmeans DNS is resolved at the proxy, avoiding local DNS leaks — handy for Tor, e.g.SEARCH_PROXY_URL=socks5h://127.0.0.1:9050). Requireshttpx[socks].External sandbox relay — set
SEARCH_SANDBOX_URLto a disposable sandbox (e.g. a self-hosted agentOS/Rivet VM relay, https://agentos-sdk.dev/docs/apps/) and every fetch is rewritten to{SEARCH_SANDBOX_URL}<url-encoded target>so the local machine never touches the target site. agentOS is TS/Rivet, not Python-importable — this env-relay is the integration seam.VPN note — a VPN is OS-level: when the OS is on one, all traffic already routes through it; the proxy and sandbox options are orthogonal to that.
See .env.example for all options. No secrets live in code and URLs that might
carry credentials are never logged.
Deploy to Prefect Horizon (hosted, off your PC)
Prefect Horizon is the managed MCP hosting
platform from the FastMCP team. Deploying there runs the engine on their
infra — so all searching/scraping egresses from Prefect's IPs, not your PC —
and serves it at an OAuth-protected URL like
https://<name>.fastmcp.app/mcp. Free for personal projects.
Verify the entrypoint locally (this is exactly what Horizon sees):
.\.venv\Scripts\fastmcp inspect search_engine/server.py:mcpYou should see 2 tools (
search,delve_sources) and 1 resource (picker_view).server.pyself-bootstraps the repo root ontosys.pathso the standalone import Horizon performs resolves the package.Dependencies —
requirements.txtpinsfastmcp==4.0.0b1+fastmcp-slim==4.0.0b1explicitly (pip doesn't read pyproject's[tool.uv]constraint), plushttpx[socks]andbeautifulsoup4. Horizon auto-detects and installs it.Push to GitHub and deploy at horizon.prefect.io: sign in with GitHub, select the repo, set entrypoint
search_engine/server.py:mcp, pick a server name (sets the URL), enable Authentication (built-in OAuth), and add secrets (PERPLEXITY_API_KEY, optionallyX_NITTER_INSTANCE/SEARCH_PROXY_URL/SEARCH_SANDBOX_URL).Connect — Horizon redeploys on push and generates connection snippets for Claude/Cursor/VS Code. Verify with its Inspector / ChatMCP, and confirm egress via
delve_sourceson an IP-echo URL.
Note: Horizon is serverless (cold starts, stateless per call, ~170s timeout) — fine for this stateless engine. Its egress is Prefect's shared IP; it cannot run a host-level VPN (use a VPS instead if you ever need a dedicated VPN exit).
Example client configuration
{
"mcpServers": {
"search-engine": {
"command": "C:\\Users\\kevin\\Projekt\\MCP-SearchEngine\\.venv\\Scripts\\python.exe",
"args": ["-m", "search_engine"]
}
}
}Layout
search_engine/
├── __init__.py
├── __main__.py # python -m search_engine
├── server.py # FastMCP server: tools + ui:// resource
├── sources.py # multi-source search + scraping engine
└── ui/
└── picker.html # interactive MCP App source-picker UIThis server cannot be installed
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