MCP via the OpenAI Responses API

Autonomous coding agent right in your IDE, capable of creating/editing files, executing commands, using the browser, and more with your permission every step of the way.

PreviewShip is a frontend preview deployment and sharing platform for developers. Deploy your static site and get a shareable preview URL in seconds — no Git, no CI/CD, no complex configuration.

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.

Code at the speed of thought – Zed is a high-performance, multiplayer code editor from the creators of Atom and Tree-sitter.

StationOne is a local-first, model-agnostic AI client and orchestration layer that enables users to build, run, and manage agentic workflows across multiple LLMs and MCP connectors. It provides a unified workspace for integrating AI models, tools, and data sources through the Model Context Protocol (MCP), allowing agents to securely interact with external services like APIs, databases, and SaaS platforms. With support for custom agents, pre-prompt templates, and a curated MCP marketplace, StationOne acts as a central hub for deploying scalable, context-aware AI automation across personal and enterprise environments.

Studio is where you run MCP tools in the browser against real credentials -- see inputs, outputs, and errors before you trust an agent. Enable bundles in your workspace, then add your Hub or per-bundle streamable HTTP URL in Cursor, Claude, ChatGPT, or VS Code. Workspace-scoped credentials; optional mcpbundles CLI on PyPI.

Persistent cloud memory for AI coding agents. 14 MCP tools — store, recall, semantic search, knowledge graph. Works with Claude Code, Cursor, Windsurf, OpenClaw. One config file, zero SDK.

# 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" ] } } } ```

## Overview ### what is Chain.Love MCP? Chain.Love MCP is a hosted remote MCP server and gateway for AI agents. It provides a single endpoint for discovering and comparing Web3 infrastructure services across 50+ blockchain networks, including RPCs, indexing, oracles, storage, compute, and developer tools. ### how to use Chain.Love MCP? To use Chain.Love MCP, add the hosted endpoint to your MCP client and connect to `https://app.chain.love/mcp` over Streamable HTTP. For public use cases, the basic MCP server URL is enough. For private downstream MCPs, add credentials only when required using `x-chainlove-cred-<credentialKey>` headers. ### key features of Chain.Love MCP? - Hosted remote MCP gateway for AI agents - Single endpoint for Web3 infrastructure discovery across 50+ blockchain networks - Aggregates infrastructure options across RPCs, indexing, oracles, storage, compute, and developer tools - Streamable HTTP transport - Public documentation and onboarding resources available online ### use cases of Chain.Love MCP? - Discovering and comparing Web3 infrastructure providers across many blockchain networks - Finding RPC, indexing, oracle, storage, compute, and developer tooling options through one MCP server - Giving AI agents a single hosted integration surface for Web3 infrastructure discovery - Reducing the need to integrate many separate provider-specific endpoints ### FAQ from Chain.Love MCP? - Can Chain.Love MCP be used as a hosted remote MCP server? Yes. Chain.Love MCP is designed to be consumed as a hosted remote MCP endpoint at `https://app.chain.love/mcp`. - Does Chain.Love MCP require credentials? Not always. Some downstream integrations may require credentials, which can be passed using `x-chainlove-cred-<credentialKey>` headers when needed. - How do I know which credential header to use? You can check the open-source Chain.Love registry at `https://github.com/Chain-Love/chain-love/blob/main/references/offers/mcpservers.csv` or browse `https://app.chain.love/toolbox/mcpservers` and look for the relevant `credentialKey` value. - Where can I learn more? Landing page: `https://www.chain.love/mcp-gateway` Documentation: `https://chain-love.gitbook.io/mcp-module`

High-fidelity AI video generator specializing in cinematic realism and complex motion dynamics.

Persistent semantic memory for Claude Code, chats AND code. 74+ MCP tools, local pgvector embeddings, codebase indexing with 14 language extractors, multi-agent team coordination. Self-hosted, zero cloud dependency.

ChatMCP is an AI chat client implementing the Model Context Protocol (MCP).

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.

E2Easy is a codeless end-to-end testing platform that lets non-technical users and founders create, manage, and run automated browser tests without writing a single line of code. It combines a browser extension for manual recording with an AI-powered Claude Connector that lets you describe tests in plain English.

The new purpose-built IDE to harness magic