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MCP Fetch Server

Anthropic

80·Strong

Overall Trust Score

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.

http
api
mcp
model-context-protocol
Version: 2025.4.6
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

84
fetch success rate
88
Methodology
Fetch operation success testing
Evidence
MCP Fetch Server
Reliable HTTP fetching with standard Node.js/Python libraries
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
html to markdown accuracy
85
Methodology
Conversion quality assessment
Evidence
Turndown Library
Uses Turndown for HTML-to-Markdown conversion with high fidelity
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
timeout handling
82
Methodology
Timeout behavior testing
Evidence
Implementation Review
Configurable timeouts with graceful failure handling
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
rate limit handling
80
Methodology
Rate limiting behavior analysis
Evidence
HTTP Client Implementation
Respects robots.txt and implements basic rate limiting
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
error recovery
83
Methodology
Error handling testing
Evidence
MCP Implementation
Handles HTTP errors, redirects, and network failures gracefully
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Security

72
ssrf protection
65
Methodology
SSRF vulnerability testing
Evidence
Security Analysis
Limited SSRF protections; can fetch internal URLs if allowed
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
url validation
75
Methodology
Input validation testing
Evidence
Implementation Review
Basic URL validation but no blocklist for internal networks
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
ssl certificate validation
85
Methodology
SSL/TLS security review
Evidence
HTTPS Implementation
Validates SSL certificates by default
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
malicious content protection
68
Methodology
Content security assessment
Evidence
Security Analysis
No built-in scanning for malicious JavaScript or content
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
sensitive data exposure
70
Methodology
Data flow security analysis
Evidence
MCP Security Model
Fetched content sent directly to LLM provider without filtering
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

Privacy & Compliance

68
web content exposure
65
Methodology
Data flow analysis
Evidence
MCP Data Flow
Fetched web content transmitted to LLM provider for processing
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
user agent disclosure
72
Methodology
Privacy disclosure review
Evidence
HTTP Headers
Sends identifiable User-Agent header to visited sites
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
cookie handling
70
Methodology
Cookie management assessment
Evidence
Implementation Review
Does not persist cookies; stateless fetching only
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
third party data sharing
68
Methodology
Data sharing analysis
Evidence
LLM Provider Policies
All fetched content shared with LLM provider per their privacy policy
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
paywalled content respect
65
Methodology
Content access policy review
Evidence
Implementation Review
No authentication support; cannot bypass paywalls ethically
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Trust & Transparency

89
documentation quality
92
Methodology
Documentation completeness review
Evidence
MCP Fetch Docs
Comprehensive documentation with usage examples and configuration
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
operation visibility
88
Methodology
Logging and traceability assessment
Evidence
MCP Protocol
All fetch operations logged in MCP transaction logs
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
open source transparency
95
Methodology
Source code review
Evidence
GitHub Repository
Fully open source implementation with MIT license
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
security disclosure
85
Methodology
Security documentation review
Evidence
MCP Security Documentation
Clear disclosure of SSRF risks and data sharing implications
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Operational Excellence

86
ease of setup
90
Methodology
Setup complexity assessment
Evidence
MCP Setup Guide
Simple setup with no authentication required for basic usage
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
fetch performance
83
Methodology
Performance benchmarking
Evidence
Performance Testing
Performance depends on target website; typically 500ms-3s per fetch
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
reliability
85
Methodology
Uptime and stability analysis
Evidence
Implementation Analysis
Stable implementation with minimal dependencies
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
conversion quality
87
Methodology
Conversion accuracy testing
Evidence
Turndown Library
High-quality HTML to Markdown conversion preserving structure
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
community adoption
85
Methodology
Community activity analysis
Evidence
GitHub Community
Widely adopted as core MCP server for web content access
Date: 2025-11-16
Confidence: mediumLast verified: 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

82

Useful for fetching documentation and code examples from the web

customer support

75

Good for retrieving knowledge base articles and support documentation

content creation

88

Excellent for research, gathering sources, and content aggregation

data analysis

70

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

research assistant

92

Ideal for academic research, gathering sources, and documentation retrieval

legal compliance

60

Limited applicability; risk of exposing confidential research to LLM providers

healthcare

58

Low suitability due to privacy concerns with medical content

financial analysis

65

Moderate risk; requires careful consideration of data sensitivity

education

90

Excellent for educational research and accessing learning materials

creative writing

85

Great for research, inspiration, and gathering reference materials