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Web Fetch Server

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A Python MCP server for web research that enables fetching web pages, searching, batch-fetching, extracting links, and summarizing content via client-side sampl

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Описание

A Python MCP server for web research that enables fetching web pages, searching, batch-fetching, extracting links, and summarizing content via client-side sampling, with optional local file access.

README

An all-in-one Python MCP server for web research: fetch pages, search the web, batch-fetch, extract links, summarize via client-side sampling, and (optionally) read/write local files — all for LLM agents like Cursor. Supports local stdio and Streamable HTTP for remote access, and exercises essentially every MCP protocol capability (Tools, Resources, Prompts, Completions, Sampling, Elicitation, Roots, Progress, Logging).

Documentation:

Document What's inside
User Manual Full end-user guide: install (Docker/Linux/Windows), Cursor setup, all tools, admin GUI, config, troubleshooting
Project Documentation Full technical reference: architecture, modules, MCP APIs, admin API, security, Docker stack, testing

Features

Tools

  • fetch_url — page content as markdown, with chunked reading (start_index, max_length)
  • fetch_metadata_tool — HEAD request metadata
  • batch_fetch — fetch multiple URLs concurrently, with per-URL error isolation and progress
  • web_search — DuckDuckGo web search (no API key), with automatic fallback to a local SearXNG instance if DuckDuckGo's scrape fails
  • extract_links — structured link/image extraction from a page
  • summarize_url — asks the connected client's LLM to summarize a page (MCP sampling)
  • read_file / write_file / list_dir — sandboxed local file access (opt-in, disabled by default)

Other MCP capabilities

  • Resources: config://settings, history://recent, fetch-cache://{encoded_url}
  • Prompts: fetch, research_topic, summarize_page, extract_key_facts, compare_sources
  • Completions: URL/depth autocomplete for prompt and resource arguments
  • Elicitation: write_file confirms before overwriting an existing file
  • Roots: local file tools honor client-exposed directories in addition to FETCH_LOCAL_FILES_ROOT
  • Progress notifications: batch_fetch and summarize_url report progress as they run
  • Management GUI: web dashboard at /admin for status, config, history, cache, and tools

Security

  • SSRF protection with resolve-then-check and redirect re-validation
  • robots.txt compliance (override with ignore_robots_txt=true)
  • Optional domain allowlist
  • Local file tools sandboxed to one configured directory, path-traversal safe
  • Bearer token auth + rate limiting for HTTP mode
  • /health endpoint
  • Windows .exe build (no Python required for end users)

Quick Start

Option A: Docker — full stack (recommended for server deployment)

Runs the MCP server, admin GUI, and SearXNG together.

Linux / macOS:

cd mcp-fetch-server
cp .env.docker.example .env          # edit MCP_AUTH_TOKEN
chmod +x scripts/docker-up.sh
./scripts/docker-up.sh
# or: docker compose up -d --build

Windows (PowerShell):

cd "E:\my python projects\MCP\mcp-fetch-server"
copy .env.docker.example .env
.\scripts\docker-up.ps1
Service URL
MCP protocol http://127.0.0.1:8000/mcp
Admin GUI http://127.0.0.1:8000/admin
Health http://127.0.0.1:8000/health
SearXNG (search fallback) http://127.0.0.1:8080

Connect Cursor over HTTP (Bearer token required):

{
  "mcpServers": {
    "web-fetch": {
      "url": "http://127.0.0.1:8000/mcp",
      "headers": { "Authorization": "Bearer your-token-from-env" }
    }
  }
}

Local files for read_file/write_file map to the ./workspace folder on your host.

Option B: Windows executable (local / Cursor stdio)

cd "E:\my python projects\MCP\mcp-fetch-server"
.\dist\mcp-fetch-server.exe --transport stdio

Build the exe yourself: .\scripts\build_exe.ps1 → outputs dist\mcp-fetch-server.exe

Option C: Python + uv (development)

cd "E:\my python projects\MCP\mcp-fetch-server"
uv sync --dev
copy .env.example .env
uv run mcp-fetch-server --transport stdio

Connect to Cursor

See the User Manual — Connect to Cursor for step-by-step instructions.

Minimal .cursor/mcp.json using the executable:

{
  "mcpServers": {
    "web-fetch": {
      "command": "E:/my python projects/MCP/mcp-fetch-server/dist/mcp-fetch-server.exe",
      "args": ["--transport", "stdio"],
      "env": { "PYTHONIOENCODING": "utf-8" }
    }
  }
}

Management GUI

A built-in web dashboard lets you monitor and manage the server without using Cursor:

Mode URL
stdio (Cursor default) http://127.0.0.1:8001/admin
streamable-http http://127.0.0.1:8000/admin (same port as MCP)

The dashboard shows uptime, registered tools, redacted configuration, recent fetch history, cached page previews, and a button to clear history/cache. It auto-refreshes every 30 seconds.

If MCP_AUTH_TOKEN is set, enter it in the dashboard's auth bar (stored in your browser session only). Disable the GUI with FETCH_ADMIN_ENABLED=false.

Optional: SearXNG search fallback

web_search uses DuckDuckGo by default and falls back to SearXNG when that scrape fails. When you use Docker (docker compose up), SearXNG is started automatically and the MCP container is preconfigured to reach it at http://searxng:8080.

For local (non-Docker) use, you can still run only SearXNG:

docker compose up -d searxng

Set FETCH_SEARXNG_URL=http://localhost:8080 in .env. See searxng/settings.yml.

Remote HTTP Mode (without full Docker stack)

$env:MCP_AUTH_TOKEN = "your-long-random-token"
.\dist\mcp-fetch-server.exe --transport streamable-http --host 127.0.0.1 --port 8000
  • MCP endpoint: http://127.0.0.1:8000/mcp
  • Health check: http://127.0.0.1:8000/health

Project Structure

mcp-fetch-server/
├── dist/mcp-fetch-server.exe   # Windows executable
├── src/mcp_fetch_server/       # Source code (tools, resources, prompts, security, ...)
├── tests/                      # 84 pytest tests
├── docs/                       # User manual + technical docs
├── scripts/docker-up.sh        # Linux/macOS stack startup
├── scripts/docker-up.ps1       # Windows stack startup
├── docker-compose.yml          # Full stack: MCP server + SearXNG
├── .env.docker.example         # Environment template for Docker Compose
├── Dockerfile                  # MCP server image
├── workspace/                  # Host folder mounted for local file tools (Docker)
├── searxng/settings.yml        # SearXNG config (JSON API enabled)
├── src/mcp_fetch_server/admin.py  # Management web GUI
└── .cursor/mcp.json            # Cursor config

Development

uv run pytest
uv run ruff check .
./scripts/docker-up.sh          # Linux / macOS
docker compose down

Windows only:

.\scripts\build_exe.ps1
.\scripts\docker-up.ps1

License

MIT

from github.com/narimanamiri/mcp-fetch-server

Установка Web Fetch Server

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/narimanamiri/mcp-fetch-server

FAQ

Web Fetch Server MCP бесплатный?

Да, Web Fetch Server MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Web Fetch Server?

Нет, Web Fetch Server работает без API-ключей и переменных окружения.

Web Fetch Server — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Web Fetch Server в Claude Desktop, Claude Code или Cursor?

Открой Web Fetch Server на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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