MCP Sse Servers

A collection of Model Context Protocol (MCP) servers, providing reference implementations and community resources, supporting enterprises to access AI tools in a secure and controllable manner, including service examples such as weather and search.

Developer tools Security #AI Tools #Enterprise Security #Protocol Specification #Remote Connection

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update time : 2025-04-29

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What is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is an open protocol that allows large language models (LLMs) to access enterprise tools and data sources in a secure and controlled manner. It provides a platform for enterprises to manage and govern how employees use AI tools.

How to use MCP?

The MCP server can be integrated into existing systems through simple configuration and API interfaces, supporting multiple programming languages such as TypeScript and Python. Users can easily access authorized tools and services through these servers.

Applicable Scenarios

MCP is suitable for enterprise environments that require strict control and governance, such as the finance, medical, and legal industries. It can ensure that the use of AI tools complies with regulatory requirements and protects sensitive data.

Main Features

OAuth 2.1 Authorization Framework

Supports standardized user authentication and permission management to ensure that only authorized users can access the MCP server.

Streaming HTTP Transmission

Provides more efficient real-time data transmission, reduces network latency, and enhances the user experience.

JSON-RPC Batch Processing

Allows multiple requests to be sent simultaneously, improving processing efficiency and reducing network overhead.

Tool Annotations

Add metadata to tools to clarify their behavioral characteristics, such as read-only or writable.

Progress Notification

Updates task status in real-time, including detailed descriptive information, for easy user monitoring.

Multi-Data Type Support

Supports multiple data formats such as text, images, and audio, expanding application scenarios.

Autocompletion

Assists users in constructing requests, improving operational convenience and accuracy.

Advantages

Enhanced security and privacy protection

Cross-platform compatibility

Rich API interfaces

Powerful extensibility

Limitations

Requires a certain technical foundation for deployment

Initial configuration may be complex

Additional learning costs are required for some advanced features

How to Use

Install the MCP SDK

Select the appropriate SDK version (TypeScript or Python) according to your needs and install it via npm or pip.

Configure the Server

Edit the configuration file and set necessary parameters such as the endpoint URL and API key.

Start the Server

Run the server script to ensure that the listening port is working properly.

Usage Examples

Query Weather Forecast

Users can query the weather conditions of a specified area through the MCP server.

Search for Network Information

Utilize the search engine function provided by SearXNG to obtain the latest network information.

Frequently Asked Questions

Is the MCP server free to use?

How to ensure data security?

Which programming languages are supported?

Related Resources

MCP Official Website

Learn about the latest developments and documentation of the MCP protocol.

GitHub Code Repository

Download and contribute to MCP-related project code.

Developer Forum

Exchange experiences and technologies with other developers.

🚀 Model Context Protocol (SSE) Server

This repository contains reference implementations of the enterprise-level Model Context Protocol (MCP), specifically using SSE (Server-Sent Events), along with references to community-built servers and other resources.

For many enterprises, it's not feasible to allow every employee unrestricted and ungoverned access to AI tools. Therefore, having these MCP tools with remote connectivity (sse) is crucial. This enables enterprises to create a reliable and responsible environment for running AI-related use cases.

All the MCP servers listed in this repository demonstrate the flexibility and scalability of MCP, showing how it can play a key role in providing secure and controlled access to tools and data sources for Large Language Models (LLMs).

Each MCP server is implemented using the TypeScript MCP SDK or the Python MCP SDK.

⚠️ Important Note

The list in this README is alphabetically sorted to minimize the chances of merge conflicts when adding new items.

📚 Documentation

MCP Specifications

Two versions of the MCP specifications were released on March 26, 2025. To quickly compare the differences between the two specifications, refer to the following table:

Property	Details	2024-11-05 (Final)	2025-03-26 (Latest)
Authorization Framework	A system for managing and controlling access to the MCP server, ensuring that only authorized users or applications can interact with it.	No specific framework mentioned; only general user consent guidance provided.	A comprehensive OAuth 2.1-based framework has been added for a standardized authorization process.
Transport Layer	The method for data transmission between the client and the server.	HTTP plus Server-Sent Events (HTTP + SSE)	Replaced with Streamable HTTP, potentially offering more flexibility or improved performance.
JSON-RPC Batch Processing	The ability to send multiple JSON-RPC requests in a single message, which can reduce network overhead and improve efficiency.	Not supported	Supported, allowing batch processing of requests.
Tool Annotations	Descriptive labels or metadata for tools, indicating their behavior, such as whether they are read-only or can perform destructive operations.	Not mentioned	Added for better understanding and control of tool usage.
Progress Notifications	Notifications that inform users of the status of ongoing operations.	No `message` field	A `message` field has been added to provide descriptive status updates, enhancing the user experience and debugging capabilities.
Data Support	The types of data that the protocol can handle and transmit.	Text and images	Support for audio data has been added, expanding the range of manageable content types.
Completion Suggestion Feature	A feature that allows for providing suggestions or completing parameters in requests, which is useful for user interfaces or helping to construct requests.	Not explicitly mentioned	Added, allowing for completion suggestions.