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

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

Auto Causal Inference is a project that uses large language models (LLMs) to automatically perform causal inference. Users only need to specify the treatment variable and the outcome variable, and the system can automatically complete the full - process analysis, including variable role identification, causal graph construction, effect estimation, and model validation. The project provides two agent architectures (LangGraph and MCP) to achieve this function, which is particularly suitable for causal problem analysis in the banking scenario.

The RLM MCP Server is a large-scale context processing tool based on the Recursive Language Model pattern. It allows Claude Code to process text of over 10 million tokens through external variables, avoiding directly inputting massive content into the prompt. Through the processes of loading, chunking, sub-querying, and aggregation, it supports automatic analysis and programmatic execution, and can connect to the Claude API or the local Ollama for free inference.

AI00 RWKV Server is an efficient inference API server based on the RWKV language model, supporting Vulkan acceleration and OpenAI-compatible interfaces.

An MCP server implementation that provides inference capabilities of the Gemini language model for Claude Desktop

An MCP server based on the recursive language model pattern, which processes ultra - large - scale contexts (over 10 million tokens) through chunking, sub - queries, and local inference, supporting automatic analysis, code execution, and security filtering.