FetchV2 MCP Server
Model Context Protocol (MCP) server for web content fetching and extraction. Enables fetching webpages, extracting clean content using Trafilatura, discovering links, and batch fetching up to 10 URLs.
README
FetchV2 MCP Server
Model Context Protocol (MCP) server for web content fetching and extraction.
This MCP server provides tools to fetch webpages, extract clean content using Trafilatura, and discover links for batch processing.
Features
- Fetch Webpages: Extract clean markdown content from any URL
- Batch Fetching: Fetch up to 10 URLs in a single request
- Link Discovery: Find and filter links on any webpage
- llms.txt Support: Parse and fetch LLM-friendly documentation indexes
- Smart Extraction: Trafilatura removes boilerplate (navbars, ads, footers)
- Robots.txt Compliance: Respects robots.txt with graceful timeout handling
- Pagination Support: Handle large pages with
start_indexparameter
Prerequisites
- Install
uvfrom Astral - Install Python 3.10 or newer using
uv python install 3.10
Installation
| Cursor | VS Code |
|---|---|
| Install MCP Server | Install on VS Code |
Or configure manually in your MCP client:
{
"mcpServers": {
"fetchv2": {
"command": "uvx",
"args": ["fetchv2-mcp-server@latest"],
"disabled": false,
"autoApprove": []
}
}
}
Config file locations:
- Claude Desktop (macOS):
~/Library/Application Support/Claude/claude_desktop_config.json - Claude Desktop (Windows):
%APPDATA%\Claude\claude_desktop_config.json - Windsurf:
~/.codeium/windsurf/mcp_config.json - Kiro:
.kiro/settings/mcp.jsonin your project
Install from PyPI
# Using uv
uv add fetchv2-mcp-server
# Using pip
pip install fetchv2-mcp-server
Basic Usage
Example prompts to try:
- "Fetch the documentation from
<URL>" - "Find all links on
<docs URL>that contain 'tutorial'" - "Read these three pages and summarize the differences:
[url1, url2, url3]"
Available Tools
fetch
Fetches a webpage and extracts its main content as clean markdown.
fetch(url: str, max_length: int = 5000, start_index: int = 0) -> str
| Parameter | Type | Default | Description |
|---|---|---|---|
url |
str | required | The webpage URL to fetch |
max_length |
int | 5000 | Maximum characters to return |
start_index |
int | 0 | Character offset for pagination |
get_raw_html |
bool | false | Skip extraction, return raw HTML |
include_metadata |
bool | true | Include title, author, date |
include_tables |
bool | true | Preserve tables in markdown |
include_links |
bool | false | Preserve hyperlinks |
bypass_robots_txt |
bool | false | Skip robots.txt check |
fetch_batch
Fetches multiple webpages in a single request.
fetch_batch(urls: list[str], max_length_per_url: int = 2000) -> str
| Parameter | Type | Default | Description |
|---|---|---|---|
urls |
list[str] | required | List of URLs (max 10) |
max_length_per_url |
int | 2000 | Character limit per URL |
get_raw_html |
bool | false | Skip extraction for all URLs |
discover_links
Discovers all links on a webpage with optional filtering.
discover_links(url: str, filter_pattern: str = "") -> str
| Parameter | Type | Default | Description |
|---|---|---|---|
url |
str | required | The webpage URL to scan |
filter_pattern |
str | "" | Regex to filter links (e.g., /docs/) |
fetch_llms_txt
Fetch and parse an llms.txt file to discover LLM-friendly documentation.
fetch_llms_txt(url: str, include_content: bool = False) -> str
| Parameter | Type | Default | Description |
|---|---|---|---|
url |
str | required | URL to an llms.txt file |
include_content |
bool | false | Also fetch content of all linked pages |
max_length_per_url |
int | 2000 | When include_content=True, max chars per page |
⚠️ Important: By default, only the llms.txt index is fetched — the linked markdown files are NOT downloaded to context. Set
include_content=Trueto explicitly fetch all linked pages.
Example:
# DEFAULT: Only fetches the index (lightweight, ~1KB)
fetch_llms_txt(url="https://docs.example.com/llms.txt")
# Returns: title + list of links with descriptions
# EXPLICIT: Fetches index + all linked .md files (can be large)
fetch_llms_txt(url="https://docs.example.com/llms.txt", include_content=True)
# Returns: structure + content of all linked pages
Note: Relative URLs (e.g., /docs/guide.md) are automatically resolved to absolute URLs.
Workflow Example
Step 1: Discover relevant documentation pages
discover_links(url="https://docs.example.com/", filter_pattern="/guide/")
Step 2: Batch fetch the pages you need
fetch_batch(urls=["https://docs.example.com/guide/intro", "https://docs.example.com/guide/setup"])
Prompts
- fetch_manual - User-initiated fetch that bypasses robots.txt
- research_topic - Research a topic by fetching multiple relevant URLs
Development
# Clone and install
git clone https://github.com/praveenc/fetchv2-mcp-server.git
cd fetchv2-mcp-server
uv sync --dev
source .venv/bin/activate
# Run tests
uv run pytest
# Run with MCP Inspector
mcp dev src/fetchv2_mcp_server/server.py
# Linting and type checking
uv run ruff check .
uv run pyright
License
MIT - see LICENSE for details.
Contributing
Contributions welcome! Please see CONTRIBUTING.md for guidelines.
Support
For issues and questions, use the GitHub issue tracker.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。