Technical Analysis

The MCP Server
Landscape

A comprehensive analysis of over 15,000 Model Context Protocol servers, their architectures, and the evolving ecosystem of AI integration.

Abstract visualization of an interconnected server network

Key Findings

  • 14,000+ servers in MCPCorpus dataset
  • 20+ metadata attributes per server
  • 7,000+ security analyzed by Backslash
  • Diverse toolsets across categories

Ecosystem Scale

16,500+
mcp.so servers
6,010+
PulseMCP daily

Executive Summary

The Model Context Protocol (MCP) ecosystem has experienced explosive growth, with our comprehensive analysis identifying over 15,000 publicly available servers across multiple directories and repositories. This analysis leverages the MCPCorpus dataset as its primary source, supplemented by data from community directories like mcp.so and security analysis platforms.

Contrary to initial expectations, MCP servers are not network services requiring IP addresses and ports, but rather software components configured via commands and URLs. This fundamental understanding shaped our data collection strategy, focusing on rich metadata attributes rather than network configurations.

Key Insight

The MCP ecosystem represents a paradigm shift in AI integration, with servers acting as bridges between AI assistants and external tools, rather than traditional network services.

Data Sources

  • MCPCorpus: 14,000 servers
  • mcp.so: 16,500+ servers
  • PulseMCP: 6,010 servers
  • Backslash: 7,000+ analyzed

Methodology

Data synthesis involved aggregating multiple sources, with deduplication based on unique GitHub repository URLs and comprehensive metadata normalization across all entries.

Data Sources and Aggregation Strategy

The data collection strategy evolved from attempting real-time web scraping to leveraging pre-compiled, comprehensive datasets. This shift was necessitated by technical barriers such as security checkpoints on major directories, which prevented direct data extraction.

Primary Data Source: MCPCorpus Dataset

Dataset Overview

The MCPCorpus dataset serves as the cornerstone of this analysis, containing approximately 14,000 MCP servers and 300 MCP clients, each with over 20 normalized metadata attributes.

Servers: 14,000
Clients: 300
Attributes per entry: 20+

Data Structure

The dataset is presented in JSON format, with each server entry containing comprehensive metadata including unique identifiers, human-readable names, URLs, categories, and technical configurations.

Key Attributes:
• id, name, title, description
• author_name, category, tags
• tools, server_command
• github metadata

Secondary Data Sources

MCP Market

Lists 14,099 servers across categories like Developer Tools, API Development, and Data Science.

mcpmarket.com

PulseMCP

Features 6,010+ servers updated daily, with download metrics and release dates.

pulsemcp.com

Backslash Security

Security hub analyzing 7,000+ servers for vulnerabilities and risk assessment.

backslash.security

MCP Server Details and Attributes

Each MCP server in the dataset contains a rich set of attributes that provide comprehensive insights into its functionality, technical configuration, and integration capabilities. The data structure enables both human understanding and programmatic access.

Core Server Information

Identification

Unique ID: 7029
Machine Name: edgeone-pages-mcp
Human Title: EdgeOne Pages MCP

Classification

Category: developer-tools
Author: TencentEdgeOne
Type: server

Description Example

"An MCP service designed for deploying HTML content to EdgeOne Pages and obtaining an accessible public URL"

— EdgeOne Pages MCP Server

GitHub Integration

Repository: TencentEdgeOne/edgeone-pages-mcp
⭐ 1.2k
Stars
🍴 89
Forks
🐛 12
Issues

Technical Configuration

Server Command

# EdgeOne Pages MCP
$ npx edgeone-pages-mcp
# Time Server
$ uvx mcp-server-time
# Redis Server
$ docker run -i --rm mcp/redis {redis_url}

Configuration JSON

{
  "mcpServers": {
    "time": {
      "command": "uvx",
      "args": [
        "mcp-server-time",
        "--local-timezone=America/New_York"
      ]
    }
  }
}

SSE (Server-Sent Events) Support

EdgeOne Pages SSE URL:
http://edgeone-pages-svc.chatmcp:9593/rest
Time Server SSE URL:
http://time-svc.chatmcp:9593/rest

Tool and Functionality Details

Tool Structure

Playwright MCP Tools
  • navigate
  • click
  • type
  • screenshot
Time Server Tools
  • get_current_time
  • convert_timezone

Input Schema Example

{
  "name": "get_current_time",
  "description": "Get current time in a specific timezone",
  "inputSchema": {
    "type": "object",
    "properties": {
      "timezone": {
        "type": "string",
        "description": "IANA timezone name (e.g., America/New_York)"
      }
    },
    "required": ["timezone"]
  }
}

Output Formats

The comprehensive MCP server data is structured for multiple output formats to serve different use cases and integration requirements. Each format preserves the rich metadata while optimizing for specific consumption patterns.

CSV Format

Tabular format ideal for spreadsheet applications and data analysis tools. Each server represents a row with comprehensive attribute columns.

Column Headers:
id, name, title, description
author_name, category, url
tools, server_command
github_stars, github_forks

JSON Format

Hierarchical format preserving nested structures and relationships. Perfect for programmatic access and API integration.

Structure:
Array of server objects
Nested github metadata
Tools array with schemas
Complete configuration data

Text Format

Human-readable summary format for quick browsing and reference. Includes essential server information in accessible layout.

Included:
Server name and description
Author and category
GitHub URL
Key capabilities summary

Notable MCP Servers and Categories

The MCP ecosystem spans diverse functional categories, each serving specific integration needs. From developer tools to AI services, these servers represent the breadth of the MCP protocol's capabilities.

Developer Tools

Playwright MCP

Browser automation server by Microsoft. Enables AI assistants to control web browsers programmatically for testing, scraping, and UI analysis.

navigate, click, type, screenshot
Microsoft author

Figma Context MCP

Design platform integration allowing AI assistants to read, interpret, and modify Figma design files programmatically.

Design interpretation
Code generation from designs

Blender MCP

3D modeling and animation control through Blender's Python API. Enables AI-generated 3D content creation.

3D model generation
Animation automation

Research and Data

Brave Search MCP

Privacy-focused web search integration. Enables AI assistants to retrieve internet information while preserving user privacy.

Privacy-preserving search capabilities

Fetch MCP (Anthropic)

Web content retrieval and conversion to plain text formats. Fundamental capability with 1.9M+ weekly downloads.

1.9M+ weekly downloads (PulseMCP metrics)

Databases

PostgreSQL MCP

Structured database access with complex query capabilities. Secure interaction with relational data models.

SQL Relational

Redis MCP

High-performance in-memory data store access. Ideal for caching and real-time analytics applications.

In-Memory Key-Value

Neon MCP Server

Serverless PostgreSQL platform integration. Scalable database access without infrastructure management.

Serverless Scalable

AI and Machine Learning

EverArt MCP

AI image generation models accessible through MCP. Create high-quality images from text descriptions with artistic control.

Key Capabilities:
Text-to-Image Artistic Generation

MiniMax MCP

Large language model integration for natural language processing tasks. Text generation, translation, and summarization capabilities.

Key Capabilities:
Text Generation Translation

Security Implications

Scale of Exposure

With over 15,000 publicly available MCP servers and more than 7,000 security-analyzed entries in the Backslash Security Hub, the MCP ecosystem presents a significant attack surface that requires careful vetting.

Critical Considerations
  • • Servers act as gateways to sensitive services
  • • Vulnerability assessment is essential
  • • Provenance and trust verification required

Security Assessment Framework

Backslash Security's analysis evaluates servers based on vulnerabilities, exposure to attack vectors, and provenance, providing a risk assessment framework for the MCP ecosystem.

Vulnerability scanning
Attack surface analysis
Provenance verification

Best Practices

Authentication & Authorization

Implement proper access controls and authentication mechanisms for sensitive server operations.

Input Validation

Rigorous validation of all input parameters to prevent injection attacks and malformed requests.

Rate Limiting

Implement rate limiting to prevent abuse and ensure fair resource allocation.

Logging & Monitoring

Comprehensive logging of server activities and monitoring for suspicious behavior.

Future Outlook

As the MCP ecosystem continues to grow, security will become increasingly critical. The emergence of dedicated security hubs and analysis tools indicates the community's recognition of these challenges and commitment to addressing them.