MCP is a standardized protocol launched by Anthropic in November 2024 and open-sourced to solve the problem of seamless integration between Large Language Models (LLMs) and external data sources and tools. Simply put, it’s like a “universal interface” that allows APIs to quickly access LLM AI applications, connecting more easily and securely to a variety of resources, and breaking down the limits of data silos.
MCP, this popular concept, is not only not difficult to understand but also very good for getting started. Its practicality is very strong. If you are starting to explore the concept of MCP, I strongly recommend you bookmark this article.
What is MCP? What Makes MCP Special?
On March 31, Google CEO Sundar Pichai threw out the phrase “To MCP or not to MCP,” which sparked a hot debate. Less than four days after Sundar Pichai solicited comments online, Philipp Schmid, Senior AI Relationships Engineer at Google DeepMind, announced on X that he had added the examples using MCP to the Gemini API documentation.
Four days later, Gemini updated its API documentation to announce its access to MCP officially. By now, AI giants such as OpenAI, Google, Anthropic, and others have all thrown themselves into the arms of the “Agent Protocol” MCP.
What is MCP?
The Model Context Protocol (MCP) is an open standard for connecting AI applications to external tools, data sources, and systems. MCP provides a common protocol for models to access contexts such as functions (tools), data sources (resources), or predefined prompts for AI models.
Models can be paired with MCP servers using the tool call feature.
The core goal of MCP is to provide a unified communication standard so that AI models or applications can easily access external data (e.g., local files, databases) and remote resources (e.g., APIs, cloud services), as well as call tools to perform tasks. It’s like the “USB-C” of the AI world: with a standardized protocol, developers don’t have to write separate interfaces for each data source or tool and can just “plug it in” and use it.
MCP adopts a client-server architecture:
- MCP Client: typically an AI application (e.g., programming tools like Claude Desktop, Cursor, etc.) that is responsible for initiating requests.
- MCP server: a lightweight program that connects to specific data sources or tools and provides services via the MCP protocol.
- Communication: based on JSON-RPC 2.0, supports local (stdio) and remote (HTTP+SSE) transfer mechanisms.
Practical Example Of MCP
- Programming Scenario: You are writing code with Cursor and want to check the table structure in a database. Previously, you had to switch to a different tool, but now, Cursor can access the database and return the results directly through the MCP server.
- Life Example: Suppose you ask the AI assistant to “check the report card in my computer, calculate the average score, and send an email to notify the students who failed the test.” MCP allows the AI to read the local file, calculate the score, and then call the email tool to send the result, which is a fully automated process.
What Makes MCP Special?
We can understand MCP with the help of the words of Kris Hansen of XNet, who says that MCP is now the equivalent of HTTP in 1993. “More products adopting this standard protocol will help everyone.”
Kris’s comparison of MCP to HTTP in 1993 makes more sense. The table below compares the two.
| Aspects | HTTP 0.9 (1993) | MCP (2025) |
| Version | 0.9, Early Release | The latest version is available in 2024 |
| Primary Functions | Transmit hypertext documents, support GET methods | Provide context for AI models, connect data sources and tools |
| Architecture | Client-server, based on TCP/IP | Client-server, standardized interfaces |
| Complexity | Very simple, no headers | Very simple; no headers |
| Adoption Stage | Early, just starting to roll out | Early, tools such as Zed and Replit started to integrate |
| Potential Impact | Becomes the foundation of the World Wide Web, leading to the Web revolution | Potentially changes the way AI data is integrated and enhances AI applications |
Both are foundational protocols in their fields, both are in early stages of development, and both have the potential to change the technology landscape.
Why is MCP So Hot?
There are several reasons for the popularity of MCP:
- Standardization: In the past, AIs wanting to use external data or tools had to develop separate docking solutions, which was time-consuming and laborious. MCP provides a common protocol, which reduces development costs.
- Flexibility: It supports everything from simple file reading to complex API calls and can adapt to a variety of scenarios, such as allowing AI to operate GitHub directly, query databases, and even generate code.
- Security: Data doesn’t have to be uploaded to the cloud, and MCP servers control resources locally, reducing privacy risks.
- Ecological momentum: Since Cursor, Claude Desktop, and other tools to support MCP, the community began to rapidly build a variety of MCP servers (such as connecting to Slack, Google Drive server), and the ecosystem is rapidly expanding.
As of today (April 8, 2025), MCP is still in a rapid development phase. In early 2025, support for tools like Cursor and Cline made it heat up, and the community is actively contributing code and server implementations. However, it is not yet fully mature; for example, the configuration interface is not user-friendly enough, and remote support is still being improved. In the future, it has the potential to become the de facto standard for how AI apps interact with the outside world, but that depends on the continued development of the ecosystem.
What are MCP Servers and Clients?
In the MCP architecture:
- MCP Servers: These lightweight programs expose external resources, tools, or data sources and act as a bridge between the AI model and the specific system. They provide functionality, such as access to local files, databases, or remote APIs, through standardized MCP protocols.
- MCP Clients: They are AI applications or agents responsible for establishing a one-to-one connection with MCP Servers to obtain data or invoke tools. Clients are typically integrated into the host application that the user interacts with, such as an IDE or AI assistant.
Choosing the “best” MCP Servers and Clients depends on your specific needs, such as development environment, type of data source, or workflow complexity. Below, I’ll list the best current options and detail their benefits and scenarios.
Top 10 MCP Servers For You
In this context, “MCP” refers to platforms built around the Model Context Protocol—a framework that many developers and AI enthusiasts use to host, share, and interact with large models or specialized services. The MCP ecosystem continues to evolve, and this guide summarizes community‐driven insights as well as performance and usability factors to help you make a choice. Below is a comprehensive overview of some of the best MCP servers and compatible clients for 2025, along with recommendations and considerations for choosing the right solutions for your projects.
MCP Servers – What to Look For and Top Recommendations
- Robust Performance and Scalability:
- Look for servers that offer high uptime, efficient resource management, and are capable of handling high request volumes. Low latency and high throughput are essential if you plan to run complex or multi-user AI tasks.
- Community Feedback and Ratings:
- A great MCP server is often verified by its active community. Platforms like MCP.so serve as community-driven directories where servers are rated by users based on reliability, speed, and support. Using these ratings, you can filter for top-performing servers.
- Ease of Integration and API Support:
- Compatibility with your applications is vital. Ensure that the server supports the necessary APIs and protocols so that your chosen client tools can communicate seamlessly.
Here are the best MCP Servers recognized in 2025, based on feature richness, ease of use, community activity, and real-world application scenarios:
1. Filesystem Server
Description: Official reference implementation that allows AI models to securely access the local filesystem.
Function:
- Supports reading, writing and managing files in a specified directory.
- Access rights can be configured to ensure security.
Advantages:
- Simple and easy to use, suitable for scenarios that require local file context (e.g. analyzing documents or code).
- Seamless integration with most clients.
- Open-source, stable, and well-maintained.
Applicable scenarios:
- developers want to let AI directly manipulate project files.
- Individual users need AI to analyze local notes or documents.
Installation: npx @modelcontextprotocol/server-filesystem /path/to/allowed/files
Community Verdict: As a base server, it’s almost a must-have for getting started with MCP.
2. GitHub Server
Description: It connects to the GitHub API to provide repository management, code analysis, and PR operations.
Function:
- Get repository files, commit history, and issue list.
- Supports the creation of PR or commit code.
Advantages:
- Deep integration with development workflow, especially for programmers.
- Easy to configure; just provide a GitHub Personal Access Token (PAT).
- Plenty of extensions in the community (e.g., Gitingest-MCP for repository summarization).
Scenarios:
- AI-assisted code review or automated GitHub operations.
- Real-time synchronization of code contexts in team collaboration.
Installation: npx @modelcontextprotocol/server-github (requires configuration of GITHUB_PERSONAL_ACCESS_TOKEN).
Community Reviews: Considered one of the most powerful MCP servers in the developer ecosystem.
3. PostgreSQL Server
Description: It provides read-only access to a PostgreSQL database with support for schema checking and querying.
Functions:
- Executes SQL queries and returns structured results.
- Supports natural language to SQL conversion (client-side cooperation required).
Advantages:
- connects to enterprise-level databases and has strong data analysis capabilities.
- The default read-only design ensures security.
- Supports complex queries, suitable for business intelligence scenarios.
Applicable scenarios:
- AI needs to analyze real-time database data.
- Enterprise users want to integrate AI into existing data infrastructure.
Installation: Configured by Python SDK, database connection string is required.
Community Reviews: Stable and powerful, but a little threshold for novices in the configuration.
4. Zapier Server
Description: Officially launched by Zapier, it connects to hundreds of third-party tools and services.
Features:
- Supports triggering automated workflows (e.g., sending emails, updating Slack).
- Provides rich API integration, no need to develop separately.
Advantages:
- has wide coverage and supports almost all major SaaS platforms.
- Free to use (basic features) and has excellent scalability.
Scenario:
- complex workflows that require cross-platform automation.
- Non-developer users want to quickly integrate external tools.
Installation: Refer to the official Zapier guide; it is easy to configure.
Community Reviews: Users call it “the most amazing MCP server”, especially for Cursor and JetBrains users.
5. DuckDB Server
Description: Community-driven server to connect to DuckDB (lightweight analytic database).
Functions:
- Supports in-memory database operations with high speed.
- Can handle CSV, Parquet, and other formats.
Advantages:
- Lightweight and efficient, suitable for local data analysis.
- Faster startup than other database servers.
Scenarios:
- Data scientists or analysts working with small to medium-sized datasets.
- Scenarios that require rapid prototyping.
Installation: Pre-built versions are available from the community and can be accessed via GitHub.
Community Reviews:Loved by the data analysis community for its performance and ease of use.
Other servers worth mentioning
- Google Drive Server: Access to files in the cloud, good for document management.
- SQLite Server: Lightweight database option, suitable for personal projects.
- Docker Server: Manage containers that are suitable for DevOps scenarios.
- MCP Prime: It offers dynamic resource management, extensive security options (including built-in DDoS protection), and automated scaling functions.
- MCP Pulse: MCP Pulse is engineered with an emphasis on performance optimization. Its capabilities help minimize latency and ensure consistent performance even during peak demand.
- MCP Edge: It provides integrated support for AI model deployment, edge caching, and distributed processing, making it well-suited for high-throughput and latency-sensitive applications.
You can also find MCP servers on GitHub. Here is how to:
Open Source Address: https://github.com/punkpeye/awesome-mcp-servers
This open source project systematically organizes the use of 3000 more can be accessed MCP Server, covering browser automation, search, finance, gaming, security, scientific research and other 20 + vertical areas, including local and cloud-based services.
Best MCP Clients Worth You Try
MCP clients are the interfaces or software libraries designed to interact with MCP servers. Depending on your workflow, you might need a client with a graphical interface for testing and monitoring, a command‐line tool for integration into automated processes, or even a custom-built solution that directly utilizes the MCP API.
Types of MCP Clients Recommended for 2025:
- Command-Line Interface (CLI) Clients:
Ideal for developers and system administrators, a CLI client allows you to script interactions, run tests, and automate tasks when communicating with your MCP server(s). Open-source options (often found on GitHub) are popular and can be tailored to your needs. - Web-Based Clients:
Many users appreciate a browser-based interface that provides real-time monitoring, performance metrics, and user-friendly controls. These platforms typically integrate dashboards, quick API testing features, and direct support for adjusting connection settings. - Custom Client Frameworks:
Given the flexibility of the Model Context Protocol, many organizations choose to build their own custom clients. Leveraging the detailed API documentation available (typically provided on platforms like MCP.so), you can create tailored interfaces that integrate with your internal systems (e.g., incorporating chat functions, visual analytics, or automated alerts).
1. Claude Desktop
Description: Official Anthropic desktop application, native client for MCP.
Features:
- Supports all MCP features (tools, resources, tips).
- Supports all MCP features (tools, resources, tips).
- Provides an intuitive interface to manage servers.
Advantages:
- It is deeply optimized with Anthropic’s Claude model.
- Works out-of-the-box, no additional development required.
- Supports both local and remote servers.
Scenarios:
- General users wanting to get a quick taste of MCP.
- Developers testing custom servers.
As the “flagship” client of MCP, stability and compatibility are impeccable.
2. Cursor
Description: It is an AI-driven code editor with MCP integration support.
Functions:
- Call tools and resources via MCP to operate directly in the editor.
- Supports natural language commands to perform complex tasks.
Advantages:
- Seamless integration with programming workflows.
- It is an active community with tons of tutorials and plugins.
Scenarios:
- Programmers wanting AI-assisted coding and debugging.
- Development scenarios that require real-time access to GitHub or databases.
Cursor has been called a “developer’s must-have”, especially with Zapier Server.
3.Cline
Description: Autonomous coding agent in VS Code with MCP support.
Functions:
- Adds customization tools via natural language.
- Displays server status and error logs.
Advantages:
- Open-source and user-friendly for beginners.
- Supports sharing the server with other clients.
Scenario:
- Building AI-driven workflows in VS Code.
- Developers who need a high degree of customization.
Cline is popular with the open source community for its flexibility and ease of use.
4. Windsurf
Description: Emerging AI programming tool with MCP support.
Functions:
- Integrated development, web crawling, and automation tool.
- Supports JetBrains and Google Drive servers.
Advantages:
- Multi-modal support (text, images, etc.).
- Rapid iteration and positive community feedback.
Scenarios:
- Cross-platform development and automation tasks.
- Complex projects that require integration with multiple resources.
Although an emerging programming tool, Windsurf is considered a strong competitor to Cursor.
5. Continue
Description: It is an Open-source AI code assistant that supports all MCP features.
Functions:
- Automatically manages server connections.
- Support for multiple LLMs (Claude, Open AI , etc.).
Advantages:
- It is completely open-source and highly customizable.
- Integrates well with existing development tools.
Scenarios:
- Open source enthusiasts or users who need deep customization.
- Scenarios where multiple models work together.
Because of its openness and flexibility, Continue is favored by technology enthusiasts.
Other Model Context Protocol（MCP）Clients For Reference
1. mcpx4j
mcpx4j is a lightweight Java library developed on the basis of the Extism Chicory SDK, utilizing the pure Java WebAssembly (Wasm) runtime environment. It integrates seamlessly with various AI frameworks in the JVM ecosystem, providing extensive model support. For example, integration with frameworks such as Spring AI and LangChain4j enables developers to easily embed MCP functionality into existing applications. In addition, mcpx4j supports the Android platform, providing integration examples with models such as Gemini, demonstrating its cross-platform compatibility and flexibility.
2. MCP.run Servlets
The MCP.run platform provides a set of WebAssembly-based servlets that allow developers to extend MCP functionality to different environments. Recently, MCP.run added support for OpenAI, allowing developers to interact with OpenAI models using the Node.js library. This feature makes it easier to integrate MCP with mainstream AI services, improving user experience and development efficiency.
3. Anthropic MCP Client
Anthropic released MCP in November 2024 with the aim of simplifying the integration process of AI tools. The client acts as a universal connector, reducing the complexity for developers when integrating different system APIs. With MCP, developers can build AI-driven applications more efficiently, improving compatibility and user experience.
4. Unisys ClearPath MCP Client
Unisys’s ClearPath MCP Client for 2025 has received several upgrades that enhance compatibility with Windows Server 2025 and improve system efficiency. New features include optimizations to TCP/IP application services, compiler updates, and extensions to the MCP TapeStack, providing improved system performance and user experience.
The selection of a suitable MCP client should be evaluated based on the user’s specific application requirements, development environment, and target platform. Taken together, the above clients perform well in terms of compatibility, functionality, and user experience and can provide strong support for developers. Source
How To Choose The Best Combination of Them
The MCP (Model Connection Protocol) ecosystem is maturing, and how you combine servers and clients depends on your identity role (developer / regular user/automation scenario) and target scenario. Below are recommendations and rationale for the best combinations based on the MCP ecosystem in 2025:
If you are a developer:
- Recommended combination: Cursor + GitHub Server + PostgreSQL Server
- Rationale: Suitable for full-stack workflow of code development, version control and data analysis.
If you are a regular user:
- Recommended combination: Claude Desktop + Filesystem Server + Google Drive Server
- Rationale: It is Easy to use and covers local and cloud file management.
If you need automation:
- Recommended combo: Windsurf + Zapier Server + Docker Server
- Rationale: Cross-platform automation and container management with powerful features.
You Can Also Try This:
Breaking down the advantages of the Cursor + GitHub Server + PostgreSQL Server combination in detail
💡 Why is this combination right for developers?
✅ 1. Cursor: An AI-driven code editor
- Built-in powerful Copilot and GPT support;
- Multifunctional development environment with support for Markdown, Jupyter, Code Structure Tree, Terminal, etc.;
- Allows for quick testing of MCP Client (e.g., Python SDK, Java SDK, mcpx4j);
- It supports local running + GitHub synchronization and is ideal for rapid construction, debugging MCP tool calls, or agent interface.
✅ 2. GitHub Server (GitHub Enterprise Server or self-hosted Gitea/Forgejo)
- Code centralized management, team collaboration;
- Can be used for version control MCP client development, plug-in deployment, and Prompt project management;
- Combined with Actions to achieve automatic deployment, such as deploying MCP Server to the container; access to CI/container; access to CI/container. can access CI/CD for updating MCP WebAssembly modules or model adapters.
✅ 3. PostgreSQL Server
- It is used as a persistent backend database that can store:
- User request and AI response logs;
- Agent state/conversation context;
- Model invocation logs/feedback data;
- Prompt model versioning;
- It can be used in conjunction with ORM frameworks, such as SQLAlchemy / Prisma, to build “memory” capable projects. ORM frameworks such as SQLAlchemy / Prisma can be combined to build AI applications with “memory” capability;
- Works with MCP Client to realize intelligent data query and text generation SQL (RAG).
You can also introduce the pgvector plugin in PostgreSQL to store user content after embedding it, implement AI search or knowledge base Q&A, and encapsulate vector recall + model calls with the MCP Client.
To MCP or Not to MCP?
“To MCP or not to MCP, that was the question.
But in 2025, when minds and models intertwine — to MCP is to thrive.”
Standardized protocols are critical to the building of the entire AI ecosystem. Just as the Internet needed the HTTP protocol to lay the foundation, the AI era also needs standards like MCP to facilitate interoperability and innovation.
Looking at the trend, the answer is clear: Yes, we should go to MCP.
But not blindly “on board”, but gradually MCP, just like the Internet from ‘browser’ to “super application”, we are moving from “AI model” to “AI system”. We are not blindly “getting on board” but gradually. MCP, just like the Internet from ‘browser’ to “super application”, is moving from an “AI model” to an “AI system”.
Why are the trends pointing to MCP?
AI is moving from “models” to “systems of capabilities.” GPT, Claude, and Gemini are no longer “single dialog tools” but intelligent modules with open interfaces.
Users are not satisfied with chatting; they want to:
- Read files → Extract data → Generate charts
- Call API → Write to database → Email notification
- Automatically take notes → Organize calendars → Report to the boss
MCP is the bridge to realize this “process automation + multi-model collaboration”. Not only that, big model vendors such as Anthropic, Open AI, Google DeepMind, and Meta have been betting on the MCP architecture; AI users are also moving from “tool people” to “AI orchestrators”, the future of high-level users, not “using an AI” but like commanders. AI users are also moving from “tool people” to “AI orchestrators”, and the future high-level users are not “using an AI” but acting like commanders, and the mission of MCP is to connect each component into an efficient system.
Our suggestion:
- If you are a developer/engineer, you should immediately lay out and use MCP Clients to connect to Claude/GPT + File Server;
- If you are a product manager or startup, you should design an MCP structure, plan AI componentized applications, and consider interfaces between models and the calling order.
- If you are an academic researcher, you are highly compatible with MCP. MCP is very suitable for intelligent body research, systematic knowledge graph experiments
- If you are an automation enthusiast, now is a good time to have a low-barrier experience. Try Claude Desktop + Filesystem Server + GitHub Server quickly!
- If you are a light AI user without many application scenarios, MCP is also difficult for you. However, you should also keep an eye on it. Just use Claude/ChatGPT Desktop for now and upgrade in the future!