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Enables AI models to interact with Spinnaker deployments, pipelines, and applications through the Model Context Protocol (MCP).
Provides access to Vilnius public transport data for Large Language Models (LLMs) via the Model Context Protocol (MCP).
Provides utilities for developing a server that interacts with the Mailchimp API.
Enables communication with multiple unichat-based Model Context Protocol (MCP) servers simultaneously, aggregating their responses.
Connects AI models to the Google Blogger API for content management.
Interacts with a Model Context Protocol server, enabling users to send commands, query data, and manage resources.
Provides Yr weather data as context for Large Language Model (LLM) tools.
Manages todos and integrates with Google Calendar using natural language through OpenAI's Assistant API and Model Context Protocol (MCP).
Demonstrates how to interact with Language Model (LLM) from MCP Servers using JBang, Quarkus, and Langchain4j.
Enables AI assistants to fetch, explore, and analyze source code from any NPM package in real-time.
Provides a Model Context Protocol (MCP) server for the Anbani Georgian Language Toolkit.
Demonstrates building a stateful Model Context Protocol server with Streamable HTTP, featuring persistent sessions, full connection resumability, and an in-memory event store.
Facilitates building AI-powered applications by showcasing Spring AI's Model Completion Provider (MCP) protocol for seamless server-client communication.
Provides a unified infrastructure interaction layer for LLM Chat and Gitlab Agent services.
Provides access to RushDB's Labeled Meta Property Graph (LMPG) database, enabling comprehensive data management and advanced querying.
Connects large language models with medical and hospital management databases, enabling AI assistants to explore database schemas and execute SQL queries for real-time data retrieval.
Dynamically imports API specifications (OpenAPI, GraphQL, AsyncAPI), exposes them as tools for agents, and enhances functionality through self-learning and autonomous documentation.
Combines a sophisticated bi-temporal knowledge graph with dynamic automation tool generation to empower AI agents with persistent, time-aware memory.
Intercepts high-risk AI agent actions for human approval, ensuring control and auditability with a cryptographically-signed decision.
Expose the Sevalla PaaS API to AI agents through a streamlined two-tool Model Context Protocol (MCP) server.
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