MCPollinations is a multimodal AI service based on the Model Context Protocol (MCP) that supports generating images, text, and audio through the Pollinations API. It offers a lightweight service that does not require authentication, is compatible with multiple AI models, and supports image saving and Base64 encoding return.

A multimodal MCP server based on the Pollinations API, supporting the generation of image, text, and audio content

A server for the Pollinations.ai service interface based on the MCP protocol, providing image and text generation functions

This project demonstrates MCP poisoning attacks against AI workflows, including verification of multiple attack scenarios such as code generation pollution and financial data leakage, for educational research purposes.

Telegram MCP Server is a tool for remotely controlling AI programming assistants (Claude Code/Codex) via Telegram. It supports multi - session management, file operations, and smart polling for a truly unattended programming experience.

An experimental chat project based on the MCP protocol, implementing long polling chat functions in a two-person room by abusing the MCP tool call mechanism.

A Tavily MCP server that supports multi-API key load balancing, provides both SSE and HTTP interfaces, and can automatically poll keys to improve concurrency and availability.

An MCP proxy server that filters tools from the upstream MCP server through a regular expression deny list, solves the context pollution problem, and reduces useless tokens

An RSS MCP server based on TypeScript that supports parsing standard RSS/Atom feeds and RSSHub feeds, providing multi - instance polling and structured content output.

Flux ImageGen MCP Server is a dedicated server based on the Model Context Protocol (MCP), focusing on providing image generation and processing functions through Pollinations AI. It supports generating image URLs from text prompts, directly generating images, and managing multiple image generation models.

A tool for calculating the altitude, azimuth, rise, and set times of celestial bodies (sun, moon, planets, stars, and deep - sky objects) at any location on Earth, supporting light - pollution analysis.

This project is an MCP server based on Node.js and Express.js, used to interact with the OpenWeather API to obtain air pollution data based on latitude and longitude or city and country, and implement secure API key management.