A multi-platform job aggregation service based on the MCP architecture, supporting the acquisition and standardization of job data from mainstream recruitment websites and providing structured support for large models.

This specification defines the standard configuration method for a local MCP server. Server information is described by creating Markdown files in the specified directory for LLMs to parse and use.

This project provides a set of development toolkits, including AI - assisted templates and utility scripts, to improve development efficiency and project management standardization.

The Model Context Protocol (MCP) is an open - source protocol released by Anthropic, aiming to solve the standardization problem of connecting large language models (LLMs) with external data sources and systems. It provides a structured framework that enables models to integrate and utilize external context, thereby expanding their capabilities and improving response accuracy. MCP supports functions such as knowledge expansion, external tool calls, and pre - written prompts.

NeoCoder is an AI programming assistance system based on the Neo4j knowledge graph. It enables the interaction between AI assistants and the knowledge graph through an MCP server, providing standardized coding workflows, dynamic instruction manuals, and project memory functions. The system supports multiple operation modes (incarnations), including basic programming, scientific research, decision - making analysis, complex system modeling, knowledge graph management, and code analysis. It stores workflow templates and execution records in Neo4j to ensure the traceability and standardization of the coding process.

A Python wrapper for the LOINC API, providing medical term query and standardization services, and integrating intelligent interaction with Claude Desktop

A data comparison tool for identifying whether two sets of data come from the same subject, supporting text standardization, value comparison, and semantic analysis.

Prompt Cleaner is an MCP server based on TypeScript, providing prompt cleaning tools and health check functions. The main functions include sensitive information desensitization, structured output, client-friendly content standardization, support for configuring LLM parameters through environment variables, and following the single model strategy to ensure behavior determinacy.

MCP is an open protocol that provides standardized context interaction for AI applications, similar to the unified interface of USB-C, supporting seamless connection between LLMs and multiple data sources and tools.

This project implements a Model Context Protocol (MCP) server for standardizing the connection between AI applications and data sources, supporting querying document content and providing three core capabilities: resources, tools, and prompts.

This project implements a Model Context Protocol (MCP) server, which provides a standardized data source access method for LLMs and supports three core capabilities: resources, tools, and prompts.

A domain availability check service based on the GoDaddy test API, supporting batch queries, multi - suffix detection, and price standardization