Agentica

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.

Gateway connector between LLM agents and world data. Official Model Context Protocol server for the Sugra API, backed by a bundled endpoint catalog and operation_id calls. Works with Anthropic Claude, OpenAI GPT, Google Gemini, xAI, and any MCP-enabled IDE. Client details: Anthropic Claude: Claude Desktop, Claude Code (CLI), claude.ai (web) OpenAI GPT: ChatGPT (via MCP connector) Google Gemini: Gemini CLI, Gemini Code Assist (VS Code + JetBrains) xAI: Remote MCP Tools in xAI SDK and Responses API IDEs: VS Code (native), Cursor, Zed, Cline, Continue.dev, Windsurf Custom agents: anything built on the Python or TypeScript MCP SDK

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.

Advanced multimodal AI music generator with realistic vocals and pro-level arrangements.

Call MCP Server Tools Online

A transparent desktop overlay that gives your AI agent a visual presence on screen. Your AI picks its own body from a library of avatars, colors, and expressions or draws it's own in SVG. Windows, $4.99, 7-day free trial, no account required.

Visual Studio Code

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.

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.

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

FullMention gives you structured visibility data from ChatGPT and Gemini in one single API call per keyword. Pull brands, websites, and products simultaneously without running 20+ separate query loops. Save massive API costs while unlocking complete market dominance insights.

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.

The new purpose-built IDE to harness magic

## 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`