ha-xiaozhi-mcp

Lutra is the first MCP compatible client built for everyone

HyperChat is a Chat client that strives for openness, utilizing APIs from various LLMs to achieve the best Chat experience, as well as implementing productivity tools through the MCP protocol.

A Tiny Terminal Chat App for AI Models with MCP Client Support

Visual Studio Code

A local-only macOS Model Context Protocol server that exposes your Proton Mail account to Claude Desktop and Claude Code via Touch-ID-gated tool calls.

Enables cloud-based AI services to access local Stdio based MCP servers via HTTP requests

The new purpose-built IDE to harness magic

One hosted MCP endpoint for a large catalog of production integrations—CRM, billing, data, dev tools, and more. Connect OAuth or API keys in the dashboard, then use one MCP URL (Hub or per-bundle) in Cursor, Claude, or other MCP clients. Includes workspace-scoped credentials, the open-source mcpbundles CLI on PyPI, and optional code-execution tools on the Hub.

A standalone Python/FastAPI server that implements the Model Context Protocol (MCP) for the OPTIX threat intelligence platform. It exposes 26 analyst-friendly tools that AI assistants and programmatic consumers can use to query threat feeds, search documents and indicators, manage watchlists, triage IOCs, generate detection rules, trigger AI research, and produce intelligence reports — without needing to understand OPTIX's internal REST API.

GPT Image 2 AI is an MCP-compatible service for generating and transforming images using text prompts. It provides a simple interface for AI agents and developer tools to create visual outputs, refine images, and experiment with generative workflows in a programmatic way. The service is designed to be lightweight and easy to integrate into MCP-based environments, enabling use cases such as: text-to-image generation image refinement and variation visual prototyping workflows AI-assisted content pipelines 👉 https://gptimg2ai.net This server can be used by AI agents to generate visual assets dynamically, making it suitable for creative tooling, automation workflows, and experimental AI applications.

# archon-mcp-client A completely silent, standards-compliant [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) client bridge that natively proxies Claude Desktop `stdio` requests directly into an Archon `SSE` remote backend over HTTPS. Unlike other public MCP proxies, this package guarantees complete `stdout` JSON-RPC hygiene, preventing log pollution that crashes Claude Desktop's strict parsers. ## Usage You do not need to install this library. Just configure your `claude_desktop_config.json` to execute it remotely via `npx`. Find more information and full documentation at [https://archonspecs.dev/docs.html](https://archonspecs.dev/docs.html). ```json { "mcpServers": { "archon-mcp": { "command": "npx", "args": [ "-y", "archon-mcp-client", "https://archonspecs.dev/mcp/sse?apiKey=your_api_key_here" ] } } } ```

A web-based graphical interface for AI chat interactions with support for multiple AI models and MCP (Model Context Protocol) servers.

Image to Image AI is an AI image and video platform that lets you transform reference images or generate new ones from text. Upload one or several images, add a prompt, and get high-quality outputs in multiple aspect ratios (1:1, 16:9, 9:16, etc.) and resolutions (1K, 2K, 4K). It runs on AI Best and supports 9+ models including Nano Banana, Nano Banana Pro, GPT-4o Image, Flux Kontext, and video models like Veo. Use it for product shots, social content, concept art, or marketing—with sharper 2K imagery, 4K scaling, better text in images, and consistent characters.

Streamlined AI music engine for custom soundtracks, beats, and thematic background scores.

Fast, intuitive AI image generation platform for everyday creative inspiration.

MCPwner is a Model Context Protocol (MCP) server that integrates security testing tools into LLM-driven workflows. It provides a unified interface for secret scanning, static analysis (SAST), software composition analysis (SCA), and vulnerability research including 0-day discovery. Instead of manually chaining tools and pasting outputs into your LLM, MCPwner standardizes and streams results directly into the model's working context. This enables continuous reasoning, correlation, and attack path discovery across the entire security research lifecycle - from identifying known vulnerabilities to uncovering novel attack vectors.