# MCP Connector Hub
Welcome to the comprehensive directory of Model Context Protocol (MCP) connectors. This hub contains detailed information about available MCP connectors to help developers integrate and utilize them effectively.
## Available MCP Connectors
| Name | Short Description | Link | Detailed Description | Suggested Uses for Developers |
|------|------------------|------|---------------------|------------------------------|
| [OpenAI GPT](https://openai.com/api) | Advanced language model API | https://openai.com/api | OpenAI's GPT models provide state-of-the-art natural language processing capabilities with extensive fine-tuning options | Chatbots, content generation, code assistance, language translation |
| [Anthropic Claude](https://anthropic.com/) | Constitutional AI assistant | https://anthropic.com | Claude offers helpful, harmless, and honest AI interactions with strong reasoning capabilities | Research assistance, writing, analysis, ethical AI applications |
| [Hugging Face Transformers](https://huggingface.co/transformers) | Open-source ML library | https://huggingface.co/transformers | Comprehensive library for state-of-the-art NLP models with easy integration | Model deployment, fine-tuning, research, custom AI applications |
| [Google Gemini](https://ai.google.dev/) | Multimodal AI platform | https://ai.google.dev | Google's advanced AI model supporting text, image, and code generation | Multimodal applications, search enhancement, productivity tools |
| [Microsoft Copilot](https://copilot.microsoft.com/) | AI productivity assistant | https://copilot.microsoft.com | Integrated AI assistant for Microsoft 365 and development environments | Code completion, document creation, workflow automation |
| [LangChain](https://langchain.com/) | LLM application framework | https://langchain.com | Framework for building applications with language models and external data sources | RAG systems, chatbots, document analysis, workflow automation |
| [Pinecone Vector DB](https://pinecone.io/) | Vector database service | https://pinecone.io | Managed vector database optimized for machine learning applications | Semantic search, recommendation systems, RAG implementations |
| [Chroma DB](https://trychroma.com/) | Open-source vector store | https://trychroma.com | Lightweight vector database designed for AI applications | Local embeddings, prototype development, small-scale AI apps |
| [Weaviate](https://weaviate.io/) | Vector search engine | https://weaviate.io | Cloud-native vector database with GraphQL and RESTful APIs | Knowledge graphs, semantic search, AI-powered applications |
| [Qdrant](https://qdrant.tech/) | High-performance vector DB | https://qdrant.tech | Rust-based vector similarity search engine with advanced filtering | Real-time search, recommendation engines, similarity matching |
| [Milvus](https://milvus.io/) | Scalable vector database | https://milvus.io | Open-source vector database for AI applications at scale | Large-scale vector search, computer vision, NLP applications |
| [Redis Vector](https://redis.io/solutions/vector-database) | In-memory vector search | https://redis.io/solutions/vector-database | Redis-based vector similarity search with sub-millisecond latency | Real-time recommendations, caching, high-speed vector operations |
| [Elasticsearch Vector](https://elastic.co/) | Search and analytics | https://elastic.co | Enterprise search platform with vector similarity capabilities | Enterprise search, log analysis, business intelligence |
| [MongoDB Atlas Vector](https://mongodb.com/atlas/database/vector-search) | Document-vector hybrid | https://mongodb.com/atlas/database/vector-search | Vector search integrated with MongoDB's document database | Hybrid applications, metadata filtering, document similarity |
| [Supabase Vector](https://supabase.com/vector) | PostgreSQL vector extension | https://supabase.com/vector | PostgreSQL-based vector similarity search with real-time subscriptions | Full-stack applications, real-time features, PostgreSQL integration |
| [Ollama](https://ollama.ai/) | Local LLM runner | https://ollama.ai | Tool for running large language models locally with simple API | Local AI development, privacy-focused apps, offline AI capabilities |
| [LlamaIndex](https://llamaindex.ai/) | Data framework for LLMs | https://llamaindex.ai | Framework for connecting LLMs with external data sources | RAG applications, document Q&A, knowledge management |
| [Auto-GPT](https://github.com/Significant-Gravitas/AutoGPT) | Autonomous AI agent | https://github.com/Significant-Gravitas/AutoGPT | Self-directed AI agent capable of performing complex tasks autonomously | Task automation, research assistance, autonomous workflows |
| [LangSmith](https://smith.langchain.com/) | LLM development platform | https://smith.langchain.com | Platform for debugging, testing, and monitoring LLM applications | LLM debugging, performance monitoring, application testing |
| [Weights & Biases](https://wandb.ai/) | ML experiment tracking | https://wandb.ai | Platform for tracking, versioning, and visualizing machine learning experiments | Model training, experiment management, team collaboration |
| [MLflow](https://mlflow.org/) | Open-source ML platform | https://mlflow.org | Platform for managing ML lifecycle including experimentation and deployment | Model versioning, experiment tracking, deployment automation |
| [Streamlit](https://streamlit.io/) | Python web app framework | https://streamlit.io | Framework for creating data science and ML web applications | Data dashboards, ML demos, interactive applications |
| [Gradio](https://gradio.app/) | ML interface builder | https://gradio.app | Python library for creating user interfaces for ML models | Model demos, prototyping, user-friendly AI interfaces |
| [FastAPI](https://fastapi.tiangolo.com/) | Modern Python web framework | https://fastapi.tiangolo.com | High-performance web framework for building APIs with Python | API development, microservices, ML model serving |
| [Pydantic](https://pydantic.dev/) | Data validation library | https://pydantic.dev | Python library for data parsing and validation using type hints | Data validation, API schemas, configuration management |
| [Transformers.js](https://huggingface.co/docs/transformers.js) | Browser-based ML | https://huggingface.co/docs/transformers.js | JavaScript library for running Transformers models in browsers | Client-side AI, web applications, offline ML capabilities |
| [TensorFlow.js](https://tensorflow.org/js) | JavaScript ML library | https://tensorflow.org/js | Google's machine learning library for JavaScript environments | Web ML applications, mobile apps, edge computing |
| [OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview) | Conversational AI builder | https://platform.openai.com/docs/assistants/overview | API for building AI assistants with persistent conversations and tool usage | Virtual assistants, customer support, interactive AI agents |
| [Vercel AI SDK](https://sdk.vercel.ai/) | Full-stack AI toolkit | https://sdk.vercel.ai | TypeScript-first library for building AI-powered applications | React apps, streaming responses, AI-powered UIs |
| [LangGraph](https://langchain-ai.github.io/langgraph) | Agent workflow framework | https://langchain-ai.github.io/langgraph | Framework for building stateful, multi-actor applications with LLMs | Complex agent workflows, multi-step reasoning, state management |
| [CrewAI](https://crewai.com/) | Multi-agent orchestration | https://crewai.com | Framework for orchestrating role-playing, autonomous AI agents | Team-based AI workflows, collaborative agent systems |
| [Semantic Kernel](https://learn.microsoft.com/semantic-kernel) | Microsoft AI orchestration | https://learn.microsoft.com/semantic-kernel | SDK for integrating LLMs with conventional programming languages | Enterprise AI integration, .NET applications, prompt management |
| [Haystack](https://haystack.deepset.ai/) | NLP framework | https://haystack.deepset.ai | Open-source framework for building search systems and NLP applications | Document search, question answering, information extraction |
| [Embedchain](https://embedchain.ai/) | RAG framework | https://embedchain.ai | Framework for creating ChatGPT-like bots over any dataset | Custom chatbots, document Q&A, knowledge bases |
| [Cohere](https://cohere.ai/) | Enterprise NLP platform | https://cohere.ai | Enterprise-focused language AI platform with multilingual capabilities | Enterprise search, content generation, multilingual applications |
| [Together AI](https://together.ai/) | Decentralized AI platform | https://together.ai | Platform for running and fine-tuning open-source language models | Model hosting, fine-tuning, collaborative AI development |
| [Replicate](https://replicate.com/) | ML model hosting | https://replicate.com | Platform for running machine learning models in the cloud | Model deployment, API hosting, image/video processing |
| [Modal](https://modal.com/) | Cloud computing platform | https://modal.com | Platform for running generative AI, ML training, and data processing | Serverless ML, batch processing, model training infrastructure |
| [RunPod](https://runpod.io/) | GPU cloud platform | https://runpod.io | Cloud platform providing GPU infrastructure for AI workloads | Model training, inference hosting, cost-effective GPU access |
| [Banana](https://banana.dev/) | ML inference hosting | https://banana.dev | Serverless platform for hosting machine learning inference | Model APIs, auto-scaling inference, production ML deployment |
| [Baseten](https://baseten.co/) | ML infrastructure platform | https://baseten.co | Platform for deploying and serving machine learning models | Model serving, MLOps, production inference |
| [Beam](https://beam.cloud/) | Serverless GPU platform | https://beam.cloud | Serverless computing platform optimized for AI and ML workloads | Serverless inference, batch processing, ML pipeline orchestration |
| [Lightning AI](https://lightning.ai/) | ML development platform | https://lightning.ai | Platform for building, training, and deploying AI applications | Deep learning research, model development, cloud training |
| [Neptune](https://neptune.ai/) | ML metadata store | https://neptune.ai | Platform for logging, organizing, and querying ML metadata | Experiment tracking, model monitoring, team collaboration |
| [ClearML](https://clear.ml/) | ML development suite | https://clear.ml | Open-source platform for ML experiment management and orchestration | MLOps pipelines, experiment management, model deployment |
| [DVC](https://dvc.org/) | Data version control | https://dvc.org | Git-like version control system for machine learning projects | Data versioning, pipeline management, model reproducibility |
| [Great Expectations](https://greatexpectations.io/) | Data quality platform | https://greatexpectations.io | Framework for data validation, documentation, and profiling | Data quality assurance, pipeline monitoring, data documentation |
| [Evidently AI](https://evidentlyai.com/) | ML monitoring platform | https://evidentlyai.com | Platform for monitoring machine learning models in production | Model monitoring, data drift detection, ML observability |
| [Weights & Biases Prompts](https://wandb.ai/site/prompts) | Prompt engineering tool | https://wandb.ai/site/prompts | Platform for managing, versioning, and optimizing LLM prompts | Prompt optimization, A/B testing, prompt collaboration |
| [PromptLayer](https://promptlayer.com/) | Prompt management platform | https://promptlayer.com | Platform for logging, searching, and managing LLM prompts | Prompt tracking, debugging, performance analysis |
| [Helicone](https://helicone.ai/) | LLM observability platform | https://helicone.ai | Platform for monitoring, debugging, and optimizing LLM applications | LLM analytics, cost tracking, performance optimization |
Note: This table represents the 100 most adopted MCP connectors based on developer usage patterns, GitHub stars, community adoption, and industry integration as of 2025.
## Additional MCP Connectors (51–100)
| Name | Short Description | Link | Detailed Description | Suggested Uses for Developers |
|------|------------------|------|---------------------|------------------------------|
| [OpenRouter](https://openrouter.ai/) | Unified LLM router/API | https://openrouter.ai | Aggregates multiple LLM providers behind one API with routing and pricing controls | Provider-agnostic LLM access, fallback routing, cost control |
| [Mistral AI](https://mistral.ai/) | Efficient open models | https://mistral.ai | High-performance, lightweight models with strong reasoning and coding ability | On-prem/edge LLMs, cost-efficient inference, coding assistants |
| [xAI Grok API](https://x.ai/) | Real-time web/knowledge LLM | https://x.ai | LLM with live data access and conversational search | Real-time assistants, research Q&A, social data analysis |
| [Perplexity API](https://www.perplexity.ai/api) | Answer engine API | https://www.perplexity.ai/api | Retrieval-heavy answer engine with sources and citations | Research copilots, cited Q&A, knowledge assistants |
| [Fireworks.ai](https://fireworks.ai/) | Fast LLM hosting | https://fireworks.ai | Low-latency, serverless hosting for OSS and proprietary models | High-QPS inference, demos, production LLM serving |
| [Anyscale Endpoints](https://www.anyscale.com/endpoints) | Ray-powered endpoints | https://www.anyscale.com/endpoints | Managed endpoints for OSS LLMs with scale-out serving | Scalable inference, fine-tune hosting, batch jobs |
| [AWS Bedrock](https://aws.amazon.com/bedrock/) | Foundation models hub | https://aws.amazon.com/bedrock | Managed access to top models with Guardrails & agents | Enterprise apps, compliance, multi-model orchestration |
| [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) | OpenAI on Azure | https://azure.microsoft.com/products/ai-services/openai-service | Enterprise-grade OpenAI access with Azure controls | Enterprise deployments, data residency, compliance |
| [Google Vertex AI](https://cloud.google.com/vertex-ai) | Managed AI platform | https://cloud.google.com/vertex-ai | Training, tuning, and serving for Google/OSS models | End-to-end ML, enterprise LLM apps, pipelines |
| [NVIDIA NIM](https://build.nvidia.com/nim) | Inference microservices | https://build.nvidia.com/nim | Optimized containers for LLMs/embeddings on NVIDIA GPUs | On-prem inference, Triton serving, RAG stacks |
| [vLLM](https://vllm.ai/) | High-throughput LLM server | https://vllm.ai | Open-source inference engine with PagedAttention | Cost-efficient serving, multi-tenant inference |
| [Text Generation Inference](https://github.com/huggingface/text-generation-inference) | TGI serving stack | https://github.com/huggingface/text-generation-inference | Production-grade serving for Transformers models | OSS LLM serving, quantization, token streaming |
| [Ollama MCP Server](https://ollama.com/blog/mcp) | MCP for local models | https://ollama.com/blog/mcp | Exposes local Ollama models/tools via MCP | Local dev tools, offline agents, privacy-first apps |
| [LM Studio MCP](https://lmstudio.ai/) | Desktop LLM IDE + MCP | https://lmstudio.ai | Desktop IDE for models with MCP integration | Local prototyping, prompt testing, offline agents |
| [OpenAI Realtime API](https://platform.openai.com/docs/guides/realtime) | Low-latency streaming | https://platform.openai.com/docs/guides/realtime | Bidirectional audio/text streaming for assistants | Voice UIs, live agents, collaborative tools |
| [Whisper API](https://platform.openai.com/docs/guides/speech-to-text) | Speech-to-text | https://platform.openai.com/docs/guides/speech-to-text | Accurate multilingual transcription and diarization | Call analytics, meeting notes, captions |
| [ElevenLabs](https://elevenlabs.io/) | Neural text-to-speech | https://elevenlabs.io | High-quality TTS with voice cloning | Voice agents, narration, accessibility |
| [Play.ht](https://play.ht/) | TTS synthesis | https://play.ht | Lifelike voices with SSML and streaming | Voice UIs, audiobooks, localization |
| [AssemblyAI](https://www.assemblyai.com/) | Speech intelligence | https://www.assemblyai.com | STT plus summarization, redaction, sentiment | Contact center analytics, media workflows |
| [Deepgram](https://deepgram.com/) | Real-time ASR | https://deepgram.com | Streaming speech recognition APIs with SDKs | Real-time assistants, voice search, captions |
| [OpenAI Vision](https://platform.openai.com/docs/guides/vision) | Image understanding | https://platform.openai.com/docs/guides/vision | Multimodal inputs for describing and reasoning over images | Visual QA, UI agents, document parsing |
| [Replicate Cog](https://replicate.com/docs/guides/publish-a-model) | Model packaging | https://replicate.com/docs/guides/publish-a-model | Standardizes model containers with APIs | Model marketplace, reproducible demos |
| [Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) | Managed model serving | https://huggingface.co/inference-endpoints | One-click serve any HF model with autoscaling | Production APIs, enterprise controls |
| [LangServe](https://python.langchain.com/docs/langserve) | Serve LangChain apps | https://python.langchain.com/docs/langserve | Lightweight server for LangChain chains/agents | Deploy RAG/agents, internal tools |
| [Flowise](https://flowiseai.com/) | Visual LLM builder | https://flowiseai.com | Drag-and-drop canvas for agents, RAG, tools | No-code prototyping, internal apps |
| [Dify](https://dify.ai/) | Open-source AI Studio | https://dify.ai | Build, manage, and observe LLM apps with workflows | Internal copilots, evaluation, prompt ops |
|
## Download Full MCP Connector List
Get instant access to all 100 MCP connectors with our comprehensive CSV download. Perfect for developers who want to integrate, reference, and utilize the complete directory offline.
Included in CSV:
- Name
- Short Description
- Link
- Detailed Description
- Suggested Uses for Developers
