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This project solves the problem of enabling seamless integration between LLM clients like Claude Desktop and specialized tools for information retrieval from AI/ML documentation and vector database management, allowing AI assistants to access and query relevant data efficiently.

This project solves the problem of simulating complex systems like ecosystems using LLMs, providing an interactive interface to build, evaluate, and analyze agent-based simulations such as predator-prey dynamics.

Provides structured, queryable access to ZVV public transport GTFS data via Spice.ai MCP for AI/LLM projects and data analyses, eliminating UI overhead and simplifying data integration.

Simplifies the implementation of Model Context Protocol (MCP) servers within NestJS applications, enabling real-time communication via Server-Sent Events (SSE) and seamless tool execution with validation.

Provides a standardized MCP (Model Context Protocol) server for the Google Cloud Profiler API, enabling AI models and applications to easily access and interact with cloud profiling services via an auto-generated interface from OpenAPI specifications.

Enables LLMs like Claude Desktop to query live Snowflake data through natural language without requiring SQL knowledge, by wrapping CData JDBC Drivers in a read-only MCP server interface.

Facilitates integration of external HTTP services providing creator exercises and products data through a standardized MCP tool-calling interface for AI or automated clients.

Provides an MCP server for integrating web scraping, RSS feed processing, and Google search tools into AI applications, enabling access to real-time web data.

Automates the generation of Behavior-Driven Development (BDD) test plans in Gherkin syntax from Figma design files, reducing manual effort in creating design-aligned test cases for development and testing teams.

Provides an MCP (Model Context Protocol) Server for interacting with Amazon EC2 Container Registry (ECR) APIs, auto-generated from its OpenAPI specification to enable standardized integration for AI models and tools.

Automates the analysis of legal contracts using AI to process PDF documents, identify risks with severity grading, discover opportunities with impact ratings, and provide summaries and recommendations, reducing manual review time and improving accuracy for legal document handling.

Provides a simple C# example of an MCP (Model Context Protocol) server tool to fetch weather forecasts, addressing the scarcity of C# MCP implementations.

Demonstrates the Model Context Protocol (MCP) for enabling AI assistants to interact with external tools and services, specifically integrating with GitHub API to create repositories programmatically.

Provides an MCP (Model Context Protocol) Server interface for the Google Cloud Runtime Configuration API, enabling seamless integration and interaction with the API via MCP protocol for AI models and applications.

Provides an MCP (Model Context Protocol) server interface for the Google Cloud Composer API, enabling seamless integration of cloud orchestration services with AI models or LLMs by auto-generating the server from an OpenAPI specification.

Implements an MCP (Model Context Protocol) server for AWS Service Catalog APIs, enabling standardized, protocol-compliant access to service catalog management functionalities from OpenAPI specifications.

Enables AI assistants to discover and access a centralized library of structured markdown instructions through a standardized Model Context Protocol (MCP) interface, solving the problem of disorganized or hardcoded prompting knowledge.

Simplifies the management of virtual machines in VMware ESXi and vCenter environments by providing a RESTful API interface based on the Model Control Protocol (MCP) for automation and integration.

Provides setup instructions for enabling GitHub MCP Server in VS Code using Docker, simplifying integration for GitHub-related development workflows.

Solves the issue of MCP clients and AI models proceeding too quickly after time-dependent actions, such as web rendering or file operations, by providing a 'wait' tool to introduce deliberate pauses and ensure asynchronous effects complete.