MCP server - chunhualiao/public-docs GitHub Wiki

Cine Extension via OpenRouter

  • MCP servers list sort by stars
    • Brave Search
    • Markdownify: converting almost anything to Markdown

Finding MCP Servers for AI Application Development

The Model Context Protocol (MCP) has rapidly emerged as a crucial building block for AI application development, creating standardized connections between AI models and data sources. Since its open-sourcing by Anthropic in November 2024, MCP has seen remarkable industry adoption across major IDEs and AI tools. For developers looking to enhance their AI applications with MCP servers, there are now several established repositories and directories where these servers can be discovered and accessed.

Understanding the Model Context Protocol

MCP is an open-source protocol developed by Anthropic that enables AI systems like Claude to securely connect with various data sources. Often described as the "USB-C port of AI applications," it creates a universal extension point for Large Language Models (LLMs) and development tools[1]. The protocol provides a standardized way for AI assistants to access external data, tools, and prompts through a client-server architecture[2].

The protocol was initially developed internally at Anthropic in July 2024, with the first working MCP integration implemented for Claude Desktop within six weeks. After successful internal testing, including creative applications like an MCP server controlling a 3D printer, Anthropic open-sourced the MCP Protocol on November 25, 2024[1][3]. The initial release included comprehensive documentation, the protocol specification, SDKs for multiple programming languages (Python, TypeScript, Java, Kotlin, and C#), and reference implementations for both servers and clients[1].

How MCP Works

MCP servers operate through a simple client-server architecture. They expose data and tools through a standardized protocol, maintaining secure 1:1 connections with clients inside host applications like Claude Desktop[2]. This approach allows AI assistants to access real-time information from various sources while maintaining security boundaries.

These servers can provide a range of capabilities to AI systems, including:

  • Sharing resources like files, documents, and data
  • Exposing tools through API integrations and actions
  • Providing prompts for templated interactions
  • Controlling access to their own resources while maintaining clear system boundaries for security[2]

Primary Directories for Finding MCP Servers

MCP Server Directories

  • Official MCP Server Directory: Visit claudemcp.com/servers to browse officially listed MCP servers.
  • GitHub Repositories: Many MCP servers are open-source and hosted on GitHub. Search for "mcp server" or specific capabilities like "mcp server web search".
  • Community Forums: Platforms like the MCP Discord channel or Anthropic's developer forums often have announcements about new servers.

MCP.so Platform

One of the most comprehensive resources for discovering MCP servers is mcp.so, a community-driven platform specifically designed to collect and organize third-party MCP servers[2]. This central directory allows developers to:

  • Search and discover various MCP servers
  • Learn about available servers' capabilities and features
  • Find information about connecting to these servers
  • Submit their own MCP servers to the directory[2]

The platform serves as an excellent starting point for developers looking to enhance their AI applications with specialized MCP servers. To submit your own server to mcp.so, you can create a new issue in their GitHub repository by clicking the 'Submit' button in the navigation bar or visiting their GitHub issues page directly[2].

GitHub Repositories

Awesome MCP Servers

The "awesome-mcp-servers" GitHub repository maintained by user punkpeye is another valuable resource for finding MCP servers[4]. This curated list focuses on both production-ready and experimental MCP servers that extend AI capabilities through various integrations like:

  • File access
  • Database connections
  • API integrations
  • And many more specialized functions[4]

This repository organizes servers into detailed categories with descriptions of their functionality, making it easier to find implementations suitable for specific use cases. The repository also notes that it syncs with a web-based directory, providing multiple ways to access this information[4].

Open-MCP.org

There's also an open-source registry at open-mcp.org that allows developers to "turn a web API into an MCP server in 10 seconds and add it to the open source registry"[4]. This provides a straightforward way to both create and register new MCP servers.

Categories of Available MCP Servers

The MCP ecosystem already offers a diverse range of server implementations across numerous categories. Based on the GitHub repository classification, these include[4]:

Aggregators

These servers provide access to many apps and tools through a single MCP server. Examples include:

  • julien040/anyquery: Query more than 40 apps with one binary using SQL
  • PipedreamHQ/pipedream: Connect with 2,500 APIs and 8,000+ prebuilt tools
  • OpenMCP: Turn web APIs into MCP servers quickly
  • MetaMCP: A unified middleware MCP server that manages connections with GUI[4]

Database Connectors

Numerous MCP servers provide connections to various database systems:

  • bytebase/dbhub: Universal database MCP server supporting mainstream databases
  • c4pt0r/mcp-server-tidb: TiDB database integration
  • f4ww4z/mcp-mysql-server: MySQL database operations
  • supabase-community/supabase-mcp: Official Supabase MCP server
  • weaviate/mcp-server-weaviate: Vector database integration
  • alexanderzuev/supabase-mcp-server: Supabase support for SQL queries[4]

File Systems

MCP servers for file access provide direct connections to local and cloud storage systems:

  • modelcontextprotocol/server-filesystem: Direct local file system access
  • modelcontextprotocol/server-google-drive: Google Drive integration
  • hmk/box-mcp-server: Box integration for file management
  • mark3labs/mcp-filesystem-server: Golang implementation for local file system access
  • Xuanwo/mcp-server-opendal: Access to any storage with Apache OpenDAL[4]

Cloud Infrastructure Management

Several MCP servers focus on cloud infrastructure:

  • nwiizo/tfmcp: A Terraform MCP server for managing infrastructure
  • rohitg00/kubectl-mcp-server: Kubernetes cluster interaction
  • strowk/mcp-k8s-go: Kubernetes operations
  • weibaohui/k8m: Multi-cluster Kubernetes management
  • erikhoward/adls-mcp-server: Azure Data Lake Storage management[4]

This extensive variety of server implementations demonstrates the flexibility of the MCP protocol and its applicability across diverse domains in AI application development.

Implementing MCP in Development Environments

The rapid adoption of MCP across major development environments makes it increasingly accessible to developers. Within just four months of being open-sourced, MCP gained support in virtually all major IDEs[1]:

  • VS Code added MCP support
  • GitHub launched its official MCP server in public preview
  • Zapier introduced a list of MCP servers
  • CI/CD services like Bitrise and CircleCI launched their MCP servers

The only notable IDE currently pending integration is JetBrains IDEs, which is expected to introduce MCP support soon[1]. This wide adoption reflects the significant benefits MCP provides for developers working with AI tools.

It's worth noting that the IDEs most frequently mentioned by engineers as having excellent AI functionality—Cursor, VS Code, Windsurf, Zed, Neovim, and Cline—were among the first to implement MCP support[1]. This correlation suggests that MCP integration is becoming a standard feature for development environments focused on advanced AI capabilities.

Choosing the Right MCP Server

With such a diverse ecosystem of MCP servers available, developers should consider several factors when selecting servers for their AI applications:

Use Case Alignment

Different MCP servers specialize in specific domains like database access, file system management, or cloud infrastructure. Developers should choose servers that align with their application's specific needs and the data sources they need to integrate.

Security Considerations

Security is built into the MCP protocol's design. Each server controls its own resources, eliminating the need to share API keys with LLM providers, and maintaining clear system boundaries. When selecting an MCP server, verify that it properly implements the protocol's security features and provides appropriate authentication and access control mechanisms[2].

Development Status

Some MCP servers are production-ready while others are experimental. For critical applications, developers should prioritize more established implementations with active maintenance and community support.

Integration Requirements

Consider how easily the MCP server can be integrated into your existing development workflow and what additional configuration might be needed for your specific environment.

Conclusion

The Model Context Protocol represents a significant advancement in AI application development by standardizing how AI assistants connect to external data sources and tools. For developers looking to implement MCP in their applications, there are now several comprehensive directories and repositories where MCP servers can be discovered, including mcp.so, the awesome-mcp-servers GitHub repository, and open-mcp.org.

The ecosystem offers a wide variety of server implementations covering databases, file systems, cloud infrastructure, and many other specialized domains. This diversity allows developers to select servers that precisely match their application requirements, enhancing their AI tools with secure access to external data and functionality.

As MCP continues to gain adoption across major development environments, it's becoming an essential component of the AI application development toolkit. By leveraging these available resources and carefully selecting appropriate MCP servers, developers can significantly enhance the capabilities of their AI applications while maintaining security and standardization.

Citations: [1] https://newsletter.pragmaticengineer.com/p/mcp [2] https://mcp.so/ [3] https://www.anthropic.com/news/model-context-protocol [4] https://github.com/punkpeye/awesome-mcp-servers [5] https://dev.to/pavanbelagatti/model-context-protocol-mcp-8-mcp-servers-every-developer-should-try-5hm2 [6] https://dev.to/techgirl1908/my-favorite-mcp-directories-573n [7] https://a16z.com/a-deep-dive-into-mcp-and-the-future-of-ai-tooling/ [8] https://huggingface.co/blog/lynn-mikami/awesome-mcp-servers [9] https://devblogs.microsoft.com/dotnet/build-a-model-context-protocol-mcp-server-in-csharp/ [10] https://github.com/modelcontextprotocol/servers [11] https://www.philschmid.de/mcp-introduction [12] https://portkey.ai/mcp-servers [13] https://modelcontextprotocol.io/introduction [14] https://www.pulsemcp.com/servers [15] https://cursor.directory/mcp [16] https://glama.ai/mcp/servers [17] https://mcp.so/servers

Answer from Perplexity: pplx.ai/share