Fable5

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

Context-optimized MCP server for DTC e-commerce brands. Connect Claude (or any MCP client) to your Klaviyo and Shopify data with 16 pre-built analytics tools. Unlike raw API wrappers, dtc-mcp pre-aggregates data server-side and returns only actionable fields — using ~80% less context than dumping raw API responses into your conversation. Features 8 Klaviyo tools — campaign performance, flow breakdowns, subscriber health, profile search, event activity 7 Shopify tools — sales summaries, daily/weekly/monthly trends, product performance, inventory alerts, customer cohorts, order search 2 cross-platform tools — email revenue attribution, full DTC health dashboard Dual revenue metrics — both gross and net revenue on every sales query Smart fallbacks — ShopifyQL when available, GraphQL pagination when not Aggressive caching — respects Klaviyo's strict rate limits (1 req/s on reporting)

The new purpose-built IDE to harness magic

Official npm package for Misar.Blog MCP — install with `npx -y @misarblog/mcp@latest`

MCP server with 188 tools for 36 U.S. government APIs. Access Treasury, FRED, Congress, FDA, CDC, FEC, lobbying disclosures, SEC, USGS, and many more. Cross-references data automatically across sources. 18 APIs need no key.

Rust MCP server that gives AI agents reliable web access. Bypasses Cloudflare and bot protection using Chrome-level TLS fingerprinting, no headless browser needed. 10 tools for scraping, crawling, search, structured extraction, summarization, and more. Output is clean markdown optimized for LLMs with 67% fewer tokens than raw HTML. 8 of 10 tools work locally without an API key.

Claude Fable 5: Anthropic's first public Mythos-class model, explained

## 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`

Open-source AI Agent for VS Code and JetBrains that autonomously solves coding tasks end-to-end.

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.

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.

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

🍒 Cherry Studio is a desktop client that supports for multiple LLM providers.

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

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

Manage your LinkaGoGo bookmarks through any AI assistant that supports the Model Context Protocol (MCP). Search, add, organize, tag, move, and export bookmarks conversationally — 16 tools for full bookmark and folder management. Connect via Claude.ai, Claude Desktop, or any MCP-compatible client.

Call MCP Server Tools Online

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.

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.