LLMling is a Python library that provides a configurable LLM task processing framework, supporting multiple context processors and LLM provider integrations.

pforge is a framework for declaratively building MCP servers using YAML configuration. Based on the Rust pmcp SDK, it supports generating optimized Rust code through configuration files and provides multiple tool processor types and language bridging functions.

The JS MCP Server is a server component based on JavaScript and WASM, providing an easy - to - integrate SDK and supporting custom processors (tools) and interactions with external services (such as Postgres and HTTP).

An MCP server that provides image retrieval and processing functions, supporting loading images from URLs, local paths, and numpy arrays, and returning base64-encoded strings and MIME types.

An MCP server that provides email processing capabilities, integrating MongoDB semantic search and SQLite efficient storage

MCP service for converting Figma designs to React applications

The Nutrient DWS MCP Server is a model context protocol server integrated with the Nutrient Document Web Service processor API, providing powerful PDF processing functions for AI assistants, including digital signatures, document generation, editing, OCR, watermarking, redaction, and other operations.

The MCP server is a multi-functional command processor that provides API services for US dollar exchange rates, weather forecasts, and news digests.

This project implements an advanced sequential thinking process based on the Multi - Agent System (MAS), which is built using the Agno framework and provided services by MCP. Compared with simple state - tracking methods, it uses coordinated specialized agents for in - depth analysis and problem decomposition, evolving from a passive thinking recorder to an active thinking processor.

This project provides documentation and utilities for IDA Pro's MCP (Machine Code Processor), aiming to help developers learn to use MCP and implement automated scripts for IDA Pro.

An MCP server framework based on PHP, supporting service definition through annotations and providing multiple processors and transmission methods.

A collection of MCP servers based on Deno and TypeScript, providing various extension tools and resources for AI models.

This project builds an AI system based on Nasdanika capabilities, focusing on operating on resource collections (interconnected models). It describes model elements and their relationships from multiple angles through the 'narrator' processor, and uses embeddings and vector storage to implement semantic search and RAG (Retrieval - Augmented Generation). It also supports the chat completion functions of OpenAI and Ollama.