MCP Tasks

Browser automation and web scraping

MCP Advisor & Installation - Use the right MCP server for your needs

Read-only database access with schema inspection

Retrieving and analyzing issues from Sentry.io

Ready for a new way to bill pay? Pay any bill in a snap, right from a chat. Describe or snap a photo of your bill (electric, water, gas, internet, phone, medical, credit card, rent, parking tickets, traffic violations and more) and Papaya reads it, then hands you a secure link to pay by card. Fast, secure, and no juggling twelve logins, with full or partial payments and status updates. Powered by Papaya (papayapay.com).

基于七牛云产品构建的 Model Context Protocol (MCP) Server，支持用户在 AI 大模型客户端的上下文中通过该 MCP Server 来访问七牛云存储资源、利用 Dora 服务进行图片操作等。 如果有什么需求欢迎在下方评论，您也可以在 github 仓库中提 issue。

AI image generation using various models

An MCP server implementation for retrieving information from the AWS Knowledge Base using the Bedrock Agent Runtime.

A Model Context Protocol (MCP) server that integrates with Jina AI Search Foundation APIs.

百度地图核心API现已全面兼容MCP协议，是国内首家兼容MCP协议的地图服务商。

MCP server to provide Figma layout information to AI coding agents like Cursor

Write notes to Flomo

Create a remote sandbox that can execute code/run commands/upload and download files. 创建远程沙盒，可以执行代码/运行命令/上传下载文件

Persistent Adaptive Planning Intelligence - structured loop engineering for AI coding assistants, with memory that persists across sessions, tools, and teammates.

A Serper MCP Server

GBOX provides environments for AI Agents to operate computer and mobile devices. Mobile Scenario: Your agents can use GBOX to develop/test android apps, or run apps on the Android to complete various tasks(mobile automation). Desktop Scenario: Your agents can use GBOX to operate desktop apps such as browser, terminal, VSCode, etc(desktop automation). MCP: You can also plug GBOX MCP to any Agent you like, such as Cursor, Claude Code. These agents will instantly get the ability to operate computer and mobile devices.

Language models generate; Concordance verifies. The verify tool checks a claim deterministically (no model in the loop) and returns HOLDS / BROKEN / INCOMPLETE with the worked reasoning and a sealed receipt (content_hash + cite_url) that re-fetches byte-identical or not at all. Also: ranked search over an ~11k-record library, seal_fetch to re-verify any receipt, redact to strip PII before text travels, and a sealed connection graph. Runs sovereign/offline too (stdlib-first Python). A public false-positive benchmark covers every domain: the engine has never sealed a falsehood. 38 tools live; remote endpoint at https://narrowhighway.com/mcp.

Official remote MCP server for Framesail AI. Create long-form (faceless YouTube) videos end to end from any MCP client: script, locked character references, storyboard, voiceover, and final video editing — with characters and style held consistent across every shot. Making long-form AI video today means 8+ tabs stitched by hand — an LLM for the script, a voice model, an image model, a video model — with characters drifting between tools and style resetting at every export. Framesail replaces the patchwork: the whole pipeline runs in one place and manages your video's context end to end. Six stages: Style (paste images, videos, or YouTube links and Framesail reverse-engineers the look, voice, and direction), Script (write it yourself or generate it in your narrative style), Reference images (auto-generated for every character, place, and prop), Voiceover (one narrator or many characters, with word-level timing), Storyboard (planned scene by scene), and Editor (captions, music, SFX, then export). No black box: you control every prompt, asset, model, and setting.