"Methods to Communicate with AI Platforms: Litellm, GroqAI, and OpenAI" matching MCP servers:

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

Lets an AI coding agent offload low-complexity, monotonous tasks to a cheap local or OpenAI-compatible model that runs its own server-side tool loop for reading, searching, and staging file writes, so large file contents and intermediate steps never enter the caller's context. Also provides summaries, extraction, classification, bulk file mapping, staged-diff review, model management, and session token-savings reporting.

A Model Context Protocol server implementation that enables connection between OpenAI APIs and MCP clients for coding assistance with features like CLI interaction, web API integration, and tool-based architecture.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables interaction with OpenAI's Chat Completion and Assistants APIs, supporting assistant management, file operations, and direct queries to GPT models through standardized MCP tools.

Production-ready MCP server that integrates OpenAI API with extensible tool support, enabling dynamic plugin loading and knowledge search capabilities through multiple interfaces including CLI and browser UI.

Query OpenAI models directly from Claude using MCP protocol.

MCP server for step-by-step mathematical reasoning and planning, enabling AI agents to execute calculations and perform GUI actions like opening PowerPoint.

Enables Claude Code agents to perform structured decision-making through TypeSafe Jev, supporting yes/no checks, route selection, scoring, and batched multi-question judgments with confidence values.

A multi-tool AI assistant system that uses Model Context Protocol to connect language models with various tools, including math calculations and weather information.

An auto-generated MCP server that enables interaction with the OpenAI API, allowing users to access OpenAI's models and capabilities through the Multi-Agent Conversation Protocol.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.

A Model Context Protocol server that converts Markdown content to HTML format.

Enables MCP clients to interact with A2A agents through four ordinary tools, translating agent discovery, messaging, task reading, and cancellation between the A2A and MCP protocols, with support for the tasks extension.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

MCP server for qualifying and responding to inbound leads in seconds using a multi-agent AI pipeline.

An MCP server that turns a one-sentence idea into working, tested code using a pipeline of six AI agents, with optional deployment.

Multi-agent swarm orchestrator for AI workflows. Exposes blackboard read/write, agent dispatch, permission gating (AuthGuardian), token budget enforcement (FederatedBudget), and audit log tools over MCP. Supports 14 AI framework adapters including LangChain, AutoGen, CrewAI, Codex, and A2A — mix any frameworks in one swarm with race-condition-safe shared state.(Traffic light for agents)