MCP Fetch Server

v2025.4.6

Anthropic

MCPhttpapimcpmodel-context-protocol
80
Strong
About This MCP

Official Anthropic MCP server for fetching web content and converting HTML to markdown. Enables AI models to retrieve and process web pages, documentation, and online resources. Essential for research, content analysis, and web-based workflows.

Last Evaluated: November 9, 2025
Official Website

Trust Vector Analysis

Dimension Breakdown

🚀Performance & Reliability
+
fetch success rate

Fetch operation success testing

Evidence
MCP Fetch Server — Reliable HTTP fetching with standard Node.js/Python libraries
highVerified: 2025-11-09
html to markdown accuracy

Conversion quality assessment

Evidence
Turndown Library — Uses Turndown for HTML-to-Markdown conversion with high fidelity
highVerified: 2025-11-09
timeout handling

Timeout behavior testing

Evidence
Implementation Review — Configurable timeouts with graceful failure handling
mediumVerified: 2025-11-09
rate limit handling

Rate limiting behavior analysis

Evidence
HTTP Client Implementation — Respects robots.txt and implements basic rate limiting
mediumVerified: 2025-11-09
error recovery

Error handling testing

Evidence
MCP Implementation — Handles HTTP errors, redirects, and network failures gracefully
mediumVerified: 2025-11-09
🛡️Security
+
ssrf protection

SSRF vulnerability testing

Evidence
Security Analysis — Limited SSRF protections; can fetch internal URLs if allowed
highVerified: 2025-11-09
url validation

Input validation testing

Evidence
Implementation Review — Basic URL validation but no blocklist for internal networks
mediumVerified: 2025-11-09
ssl certificate validation

SSL/TLS security review

Evidence
HTTPS Implementation — Validates SSL certificates by default
highVerified: 2025-11-09
malicious content protection

Content security assessment

Evidence
Security Analysis — No built-in scanning for malicious JavaScript or content
mediumVerified: 2025-11-09
sensitive data exposure

Data flow security analysis

Evidence
MCP Security Model — Fetched content sent directly to LLM provider without filtering
highVerified: 2025-11-09
🔒Privacy & Compliance
+
web content exposure

Data flow analysis

Evidence
MCP Data Flow — Fetched web content transmitted to LLM provider for processing
highVerified: 2025-11-09
user agent disclosure

Privacy disclosure review

Evidence
HTTP Headers — Sends identifiable User-Agent header to visited sites
mediumVerified: 2025-11-09
cookie handling

Cookie management assessment

Evidence
Implementation Review — Does not persist cookies; stateless fetching only
mediumVerified: 2025-11-09
third party data sharing

Data sharing analysis

Evidence
LLM Provider Policies — All fetched content shared with LLM provider per their privacy policy
highVerified: 2025-11-09
paywalled content respect

Content access policy review

Evidence
Implementation Review — No authentication support; cannot bypass paywalls ethically
mediumVerified: 2025-11-09
👁️Trust & Transparency
+
documentation quality

Documentation completeness review

Evidence
MCP Fetch Docs — Comprehensive documentation with usage examples and configuration
highVerified: 2025-11-09
operation visibility

Logging and traceability assessment

Evidence
MCP Protocol — All fetch operations logged in MCP transaction logs
highVerified: 2025-11-09
open source transparency

Source code review

Evidence
GitHub Repository — Fully open source implementation with MIT license
highVerified: 2025-11-09
security disclosure

Security documentation review

Evidence
MCP Security Documentation — Clear disclosure of SSRF risks and data sharing implications
mediumVerified: 2025-11-09
⚙️Operational Excellence
+
ease of setup

Setup complexity assessment

Evidence
MCP Setup Guide — Simple setup with no authentication required for basic usage
highVerified: 2025-11-09
fetch performance

Performance benchmarking

Evidence
Performance Testing — Performance depends on target website; typically 500ms-3s per fetch
mediumVerified: 2025-11-09
reliability

Uptime and stability analysis

Evidence
Implementation Analysis — Stable implementation with minimal dependencies
highVerified: 2025-11-09
conversion quality

Conversion accuracy testing

Evidence
Turndown Library — High-quality HTML to Markdown conversion preserving structure
highVerified: 2025-11-09
community adoption

Community activity analysis

Evidence
GitHub Community — Widely adopted as core MCP server for web content access
mediumVerified: 2025-11-09
Strengths
  • +Simple, zero-configuration setup for basic web content fetching
  • +High-quality HTML to Markdown conversion using Turndown library
  • +Open source with active Anthropic support and maintenance
  • +Reliable HTTP fetching with timeout and error handling
  • +Respects robots.txt and implements ethical web scraping practices
  • +Excellent documentation and integration with MCP ecosystem
Limitations
  • !Limited SSRF protection; can potentially access internal networks
  • !All fetched content exposed to LLM provider without filtering
  • !No built-in authentication for paywalled or restricted content
  • !No malicious content scanning or sanitization
  • !Performance dependent on target website responsiveness
  • !Basic rate limiting may cause issues with aggressive scraping
Metadata
license: MIT
supported platforms
0: All platforms with Node.js/Python
programming languages
0: TypeScript
1: Python
mcp version: 1.0
github repo: https://github.com/modelcontextprotocol/servers
github stars: 58700
api dependency: HTTP/HTTPS, Turndown
authentication: None required
first release: 2024-11
maintained by: Anthropic
status: Official - Active
transport types
0: stdio
installation methods
0: npm

Use Case Ratings

code generation

Useful for fetching documentation and code examples from the web

customer support

Good for retrieving knowledge base articles and support documentation

content creation

Excellent for research, gathering sources, and content aggregation

data analysis

Useful for fetching data from web sources, but not optimized for structured data

research assistant

Ideal for academic research, gathering sources, and documentation retrieval

legal compliance

Limited applicability; risk of exposing confidential research to LLM providers

healthcare

Low suitability due to privacy concerns with medical content

financial analysis

Moderate risk; requires careful consideration of data sensitivity

education

Excellent for educational research and accessing learning materials

creative writing

Great for research, inspiration, and gathering reference materials