PulseMCP Server is a Model Context Protocol (MCP) server that provides tools for discovering and exploring MCP servers and integrations through the PulseMCP API. It supports features like listing available MCP servers with filtering and pagination, searching servers by name or functionality, filtering by integration types, and listing all available integrations. The server is built using TypeScript and offers robust error handling for various scenarios.
PulseMCP Server allows users to easily discover and explore MCP servers and their integrations. Its powerful filtering, pagination, and search capabilities make it efficient for finding specific servers or integrations. Additionally, its full TypeScript support ensures type safety and better developer experience during implementation.
PulseMCP Server is designed for developers working with MCP clients who need to interact with multiple MCP servers and integrations. It's particularly useful for teams managing complex integrations across platforms or those looking for an organized way to explore available servers and tools.
PulseMCP Server can be installed on any environment that supports Node.js. Users can clone the repository, install dependencies via npm, build the project, and run the server either directly or through npm commands.
PulseMCP Server should be used when there is a need to manage, explore, or integrate with multiple MCP servers. It is ideal for scenarios requiring dynamic discovery of servers, filtering based on specific criteria, or listing available integrations.
To install PulseMCP Server in your MCP client, add the following configuration to your client preferences: { "mcpServers": { "pulsemcp": { "command": "npx", "args": ["-y", "pulsemcp-server"] } } }. Then, clone the repository, install dependencies using `npm install`, and build the project with `npm run build`.
PulseMCP Server provides two main tools: 'list_servers', which lists MCP servers with optional filtering and pagination, and 'list_integrations', which lists all available integrations without requiring parameters.
The project structure includes: pulsemcp-server/ ├── src/ │ └── index.ts # Main server implementation ├── build/ # Compiled JavaScript ├── package.json # Project configuration └── tsconfig.json # TypeScript configuration
PulseMCP Server relies on @modelcontextprotocol/sdk (^0.6.0), axios (^1.7.9), and TypeScript (^5.3.3) as its core dependencies.
Contributions to PulseMCP Server are welcome! You can open a pull request (PR) with your proposed changes. The project encourages collaboration and rewards contributors for their efforts.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.