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Demonstrates how to set up a Model Context Protocol (MCP) server and client using Server-Sent Events (SSE) for LLM prompting.
Enables Large Language Models to query local Nostr databases using the `ndb` command-line tool.
Manages and automates interactions with Nature Remo devices through an MCP server.
Integrates with Apple Notes to create a personal memory system for AI agents, enabling easy information recall and saving on macOS.
Execute AppleScript and JavaScript for Automation (JXA) scripts on macOS remotely via a Model Context Protocol (MCP) server.
Enables AWS Lambda functions to operate as an MCP server with Server-Sent Events (SSE) support.
Orchestrates multi-agent Claude Code instances for collaborative software development tasks within a tmux session.
Provides a standardized protocol for AI agents to access Stack Overflow's vast developer knowledge base.
Enables text completion with local LLama.cpp models by acting as a Model Context Protocol server.
Provides a Model Context Protocol (MCP) server for managing and analyzing SQLite databases.
Enables programmatic introspection, task execution, and test running for Gradle projects, with integrated support for Develocity Build Scans.
Manages persistent, categorized bookmarks for AI workflows and Model Context Protocol (MCP)-compatible clients.
Provides an SDK for building AI agents with B2B SaaS integrations and an MCP server for connecting agents to various connectors.
Provides a natural language interface for agentic applications to manage, monitor, and query data in CockroachDB.
Empowers coding agents to retrieve context-relevant rules and guidelines on demand, streamlining development workflows.
Provides a Model Context Protocol (MCP) server to integrate Joe Sandbox Cloud's advanced analysis capabilities with MCP-compatible applications.
Facilitates interaction with Android devices by exposing common ADB commands as a newline-delimited JSON-RPC server.
Transforms web content into large language model (LLM) friendly formats using Jina.ai's reader service.
Orchestrates multi-agent AI research by running multiple LLM providers in parallel and intelligently synthesizing their responses.
Enables AI agents to start, stop, and monitor long-running command-line processes.
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