"Just Eat" matching MCP servers:

Just asking your favorite coding agents to drive plori agent as a sub agent.

A lightweight MCP server that provides a unified interface to various LLM providers including OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, and Ollama.

Enables agent settlement, trust verification, and ledger operations for multi-agent workflows, with tools for blueprint management, credit tracking, and provenance recording.

Enables any MCP-capable AI agent to join a shared broadcast room and exchange messages with all other connected agents in real time, using just a nickname in the URL and no accounts or permissions.

A unified MCP gateway that reduces context token usage by exposing all services through just two tools (search and execute), supporting built-in services like Asana and Sentry as well as proxied external MCP servers.

An MCP aggregator that consolidates multiple MCP servers behind a single interface with just 3 tools (search, get details, execute), reducing context pollution for AI agents by avoiding direct exposure of numerous tool schemas.

Provides intelligent summarization capabilities through a clean, extensible architecture. Mainly built for solving AI agents issues on big repositories, where large files can eat up the context window.

MCP Hub is a self-hosted AI operations platform that provides a unified MCP gateway with semantic tool routing, persistent vector memory, automation, and multi-agent flows. It enables connecting any MCP client to 130+ tools across 12 integrations through just 3 hub endpoints.

Hosted shared knowledge base for AI agents. Store, search, and retrieve structured knowledge using semantic search. Agents contribute to a growing collective intelligence that compounds over time. No install — just a URL.

A TypeScript-based MCP server that enables advanced agent conversations and workspace management via the Dust platform. It provides tools for managing messaging sessions, retrieving agent configurations, and interacting with Dust assistants.

A proof-of-concept Model Context Protocol server that enables LLM applications to interact with Uber Eats, allowing AI agents to browse and order food through natural language.

A Model Context Protocol (MCP) server for Dust.tt agents, designed for seamless integration with Claude Desktop via STDIO. Provides robust agent querying, listing, and configuration tools.

An MCP server that enables AI models to search the web using OpenAI's 4o-mini Search model, allowing access to up-to-date information for just a few cents per search.

Async MCP server for running long-running AI tasks with real-time progress monitoring, enabling users to start, monitor, and manage complex AI workflows across multiple models.

Facilitates two-stage reasoning processes using DeepSeek for detailed analysis and supports multiple response models such as Claude 3.5 Sonnet and OpenRouter, maintaining conversation context and enhancing AI-driven interactions.

A sample implementation of a Model Context Protocol server using Next.js and the Vercel MCP Adapter, allowing developers to create AI assistants with custom tools and resources.

Lets any MCP agent control your signed-in Chrome with full login state, offering normal browser tools and an optional Fast JEV mode for bounded-speed typed decisions on the same profile.

A proof-of-concept server implementing the Model Context Protocol that enables LLM applications to interact with Uber Eats functionality.