mcp-search-server
MCP-compliant server providing web search via Yandex Search API and URL fetching with fallback mechanisms, plus a monitoring dashboard.
README
MCP Search Server
MCP-compliant server providing web search (Yandex Search API) and URL fetching capabilities over JSON-RPC 2.0. Includes a built-in monitoring dashboard with live log feed and request statistics.
Quick Start
Online (with internet)
pip install git+https://github.com/in40/mcp-search-server.git
Offline (air-gapped, no internet)
Prerequisites: Python 3.10+, pip.
Download
Two bundles available on the GitHub Releases page:
| Bundle | Size | Playwright | Chromium |
|---|---|---|---|
mcp-search-server-offline.tar.gz |
21 MB | no | no |
mcp-search-server-offline-full.tar.gz |
330 MB | yes | yes |
# Light (no browser automation):
curl -sL https://github.com/in40/mcp-search-server/releases/download/v2.2.0/mcp-search-server-offline.tar.gz -o mcp-search-server-offline.tar.gz
# Full (with Playwright + Chromium for JS-heavy sites):
curl -sL https://github.com/in40/mcp-search-server/releases/download/v2.2.0/mcp-search-server-offline-full.tar.gz -o mcp-search-server-offline-full.tar.gz
To rebuild yourself (e.g. for a different Python version):
git clone https://github.com/in40/mcp-search-server.git
cd mcp-search-server
bash scripts/build-offline.sh # light (21 MB)
bash scripts/build-offline.sh --with-playwright # full (330 MB)
Deploy (on the air-gapped machine)
Light bundle:
tar xzf mcp-search-server-offline.tar.gz
cd offline
bash install.sh --venv ./venv
Full bundle (Playwright included):
tar xzf mcp-search-server-offline-full.tar.gz
cd offline
bash install.sh --venv ./venv
# install.sh automatically links bundled Chromium
Manual install (without the script)
pip install --no-index --find-links=./wheels ./wheels/mcp_search_server-*.whl
Configure & Run
export YANDEX_SEARCH_API_KEY="your-yandex-api-key"
export YANDEX_FOLDER_ID="your-yandex-folder-id"
export YANDEX_SEARCH_TYPE="SEARCH_TYPE_COM" # or SEARCH_TYPE_RU
mcp-search-server
Architecture
┌─────────────────────────────────────────────────────────────┐
│ mcp-search-server │
│ │
│ Port 8090 │ Port 8091 │
│ MCP JSON-RPC 2.0 │ REST API Dashboard │
│ ┌───────────────────┐ │ ┌──────────────┐ ┌────────────┐ │
│ │ tools/list │ │ │ POST /search │ │ GET / │ │
│ │ tools/call │ │ │ POST /fetch │ │ (HTML UI) │ │
│ │ - internet_search │ │ │ GET /health │ │ │ │
│ │ - fetch_url │ │ │ GET /stats │ │ │ │
│ └───────────────────┘ │ │ GET /logs │ └────────────┘ │
│ │ └──────────────┘ │
│ In-memory log ring buffer (24h retention) │
└─────────────────────────────────────────────────────────────┘
Features
Search (Yandex Cloud Search API)
- Primary search provider: Yandex Cloud Search API
- Fallback: Brave Search API (configurable via
SEARCH_PROVIDER) - Results include title, URL, description excerpts
URL Fetch (fetch_url)
Multi-layered fallback system:
- requests library with Chrome UA headers (primary)
- SSL fallback - retries without cert verification for broken cert chains
- curl subprocess - bypasses some WAF/bot challenges (QRATOR, Cloudflare)
- Playwright/Chromium - headless browser for JS-heavy sites
- Wikipedia API - direct API for QRATOR-protected Wikipedia mirrors
- Content extraction via trafilatura (HTML-to-Markdown)
- Russian-language error messages for all failure modes
Dashboard (Port 8091)
- Real-time stats: total requests, last-hour, errors, search vs fetch breakdown
- Live log feed with auto-refresh (5s interval)
- Per-request detail modal (full JSON)
- Yandex search tab with timing and results
- Themes: blue (default), orange (set
DASHBOARD_THEME=orange)
Environment Variables
| Variable | Required | Default | Description |
|---|---|---|---|
YANDEX_SEARCH_API_KEY |
Yes | (placeholder) | Yandex Cloud API key |
YANDEX_FOLDER_ID |
Yes | (placeholder) | Yandex Cloud folder ID |
YANDEX_SEARCH_TYPE |
No | SEARCH_TYPE_COM |
SEARCH_TYPE_RU for Russian results |
SEARCH_PROVIDER |
No | yandex |
brave to use Brave Search API |
BRAVE_SEARCH_API_KEY |
No | (placeholder) | Required if SEARCH_PROVIDER=brave |
PORT |
No | 8090 |
MCP JSON-RPC server port |
DASHBOARD_PORT |
No | 8091 |
Dashboard + REST API port |
LOG_RETENTION_HOURS |
No | 24 |
Hours to keep request logs in memory |
API_KEYS_PATH |
No | ./api_keys.json |
Path to API keys file |
DASHBOARD_THEME |
No | (none) | Set to orange for orange theme |
Authentication
Both MCP and Dashboard endpoints require X-API-Key header matching keys in api_keys.json:
{
"keys": {
"your-api-key-here": "read-write"
}
}
MCP JSON-RPC API
initialize
{"jsonrpc": "2.0", "method": "initialize", "id": 1}
tools/list
Returns two tools: internet_search and fetch_url.
tools/call
internet_search
{
"jsonrpc": "2.0",
"method": "tools/call",
"id": 2,
"params": {
"name": "internet_search",
"arguments": { "query": "MCP protocol", "count": 5 }
}
}
fetch_url
{
"jsonrpc": "2.0",
"method": "tools/call",
"id": 3,
"params": {
"name": "fetch_url",
"arguments": { "url": "https://example.com" }
}
}
REST API (Dashboard Port)
| Method | Path | Auth | Description |
|---|---|---|---|
GET |
/health |
No | Health check |
GET |
/stats |
X-API-Key | Aggregated statistics |
GET |
/logs?type=&limit= |
X-API-Key | Request logs |
GET |
/ |
X-API-Key | Dashboard HTML UI |
POST |
/api/search |
X-API-Key | JSON: {"query": "...", "count": 5} |
POST |
/api/fetch |
X-API-Key | JSON: {"url": "..."} |
Docker
Online build
FROM python:3.11-slim
RUN apt-get update && apt-get install -y --no-install-recommends curl && rm -rf /var/lib/apt/lists/*
RUN pip install --no-cache-dir \
"mcp-search-server[playwright] @ git+https://github.com/in40/mcp-search-server.git" \
&& python -m playwright install --with-deps chromium
COPY api_keys.json .
ENV API_KEYS_PATH=/app/api_keys.json
CMD ["mcp-search-server"]
Offline build (air-gapped)
Copy mcp-search-server-offline.tar.gz to the build context, then:
FROM python:3.11-slim
RUN apt-get update && apt-get install -y --no-install-recommends curl && rm -rf /var/lib/apt/lists/*
COPY mcp-search-server-offline.tar.gz /tmp/
RUN cd /tmp && tar xzf mcp-search-server-offline.tar.gz && \
pip install --no-index --find-links=/tmp/offline/wheels /tmp/offline/wheels/mcp_search_server-*.whl && \
rm -rf /tmp/mcp-search-server-offline.tar.gz /tmp/offline
COPY api_keys.json .
ENV API_KEYS_PATH=/app/api_keys.json
CMD ["mcp-search-server"]
Development
Online
git clone https://github.com/in40/mcp-search-server.git
cd mcp-search-server
python -m venv .venv && source .venv/bin/activate
pip install -e ".[all]"
playwright install --with-deps chromium
mcp-search-server
Offline
tar xzf mcp-search-server-offline.tar.gz
cd offline
bash install.sh --venv ./venv
source ./venv/bin/activate
mcp-search-server
License
Apache 2.0
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.