A Model Context Protocol server that allows AI agents to execute SQL queries against a MySQL database, supporting operations like reading data, creating tables, inserting, updating, and deleting records.
A server that implements the Model Completion Protocol (MCP) to allow LLMs to interact with Databricks resources including clusters, jobs, notebooks, and SQL execution through natural language.
Non-official Model Context Protocol server that enables interaction with AMap's location services through clients like Cursor, Claude Desktop, Cline, and Windsurf.
A ready-to-use starter implementation of the Model Context Protocol (MCP) server that enables applications to provide standardized context for LLMs with sample resources, tools, and prompts.
Enables federated search across public ARD registries and a verified index of MCP tools read from each server's own tools/list, with hybrid lexical and semantic retrieval. It lets agents discover and connect to resources at runtime without hard-coded integrations.
Delivers the LXAgents shared agent instruction set via MCP prompts and resources. It includes read-only tools for setting up and checking agent instructions in repositories.
A sample implementation of a Model Context Protocol server using Next.js and the Vercel MCP Adapter, allowing developers to create AI assistants with custom tools and resources.
A declarative framework that manages LLM resources, prompts, and tools through YAML configuration files. It enables LLMs to interact with external data, CLI commands, and Python functions using the Model Context Protocol.
A Model Context Protocol server that connects AI assistants with the Kernel platform, enabling them to deploy applications, automate web browsers, and manage cloud resources.
Enables AI tools to uniformly discover, inspect, and call tools, prompts, and resources from multiple upstream MCP servers through a small set of fixed MCP tools, over stdio or HTTP.
Ultra-lightweight MCP server for Bun with full protocol compliance. Enables building MCP servers with tools, resources, and prompts using minimal dependencies and heap footprint.
Enables enterprise AI agents to discover and invoke MCP tools, resources, and prompts across GitHub, ITSM, and business systems through a policy-controlled gateway with human approval and auditability.