# Language Model

Showcasing the integration of computer vision tools with language models through MCP

An MCP server implementation for Deepseek R1, supporting the Node.js environment and providing powerful language model inference services.

The 302AI Basic MCP Server is a toolkit that enhances the basic capabilities of large language models. It supports dynamically loading a list of remote tools, provides multiple usage modes (local stdin or remote HTTP service), and includes practical tools such as Wiki search, Arxiv search, calculator, and code execution.

The API-MARKET MCP Server is a model context protocol server that exposes over 200 API endpoints on API-Market as MCP resources, enabling large language models to discover and interact with these APIs through the MCP protocol.

This project is a collection of reference implementations of the Model Context Protocol (MCP), including MCP server implementations for various tools and services, aiming to provide secure and controllable access to tools and data sources for large language models (LLMs).

The DeepSeek MCP Server is an intermediate service used to connect the DeepSeek API with MCP-compatible applications (such as Claude Desktop), supporting various model parameter configurations and chat functions.

An MCP server implementation for Deepseek R1, supporting integration with Claude Desktop and providing powerful language model inference services.

An MCP server that provides a language model interface for the SearXNG search engine.

A TypeScript project integrating Langchain and MCP tools, supporting interaction with language models through a chat interface and execution of various tools.

An MCP server collection project for learning and experimentation, exploring different implementation methods and protocol capabilities

An MCP server implementation for the Deepseek R1 language model based on Node.js, supporting an 8192-token context window, providing stable Claude Desktop integration and model parameter configuration.

This project is an implementation of the Model Context Protocol (MCP) server, demonstrating how to build a functional server that can be integrated with multiple language model clients. As a standardized protocol, MCP provides a unified way for AI applications to connect data sources and tools, supporting three types of capabilities: resources, tools, and prompts.

The Perplexity MCP Server is a Node.js-based server that implements Anthropic's Model Context Protocol (MCP) for connecting Claude and Perplexity's language models, providing enhanced AI interaction tools.