#Embeddings MCP Servers

Use GPT-4o, DALL-E 3, embeddings, fine-tuning, and moderation as tools inside your AI agent workflows.

Manage AI inference via Mistral. Execute chat completions, generate RAG embeddings, and audit frontier models.

Access LLMs, embeddings, code generation, and reasoning via NVIDIA API Catalog.

Empower RAG via Cohere. Generate high-quality text embeddings, rerank documents for better accuracy, and perform AI classification directly from any AI agent.

Empower vector search via Elasticsearch. Perform dense vector kNN searches, handle index mappings, and index embedding documents directly from any AI agent.

Power your RAG and search via Jina AI. Generate embeddings, rerank documents, read URLs, and perform semantic web search.

Manage vector storage via MongoDB Atlas. Perform similarity searches, query MQL documents, and audit collections.

Run k-NN vector searches on OpenSearch. Create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.

Run vector similarity searches, manage embedding tables, and build AI-powered retrieval pipelines. All directly inside your existing PostgreSQL database.

Access Cohere AI models via API. Chat with Command models, generate embeddings, rerank documents and tokenize text from any AI agent.

Access Mistral AI models via API. Chat with Claude alternatives, generate embeddings, moderate content and manage batch jobs from any AI agent.

Build with European open-weight language models that deliver strong reasoning, multilingual capability, and efficient inference.

Manage vector embeddings via Chroma. List collections, query embeddings, and audit document counts directly from any AI agent.

Manage vector search and NoSQL via Couchbase. Execute N1QL queries, perform KNN vector searches, and audit documents directly from any AI agent.

Empower LLM applications via Fireworks AI. Perform ultra-fast chat completions, generate embeddings and images, and transcribe audio directly from any AI agent.

Manage vectorized data via LanceDB. Perform similarity searches, create tables, and manage multi-modal embeddings.

Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack.

Connect your AI to Supabase Vector. Execute pgvector semantic searches, manage embeddings, and run relational database queries directly from your terminal.

Bring Google's massive vector matching power to your AI agent. Search billions of semantic embeddings and administer Vertex Index endpoints directly in chat.

Search the web with neural embeddings that understand meaning, not just keywords, and return the most relevant results for any query.

Manage vector storage via Milvus. Perform ANN searches, query scalar entities, and audit collections.

Power your agent with Vald. Query, insert, and manage dense vectors on a highly scalable, distributed nearest-neighbor engine.

Orchestrate Baidu Qianfan AI models. Manage chat completions, embeddings, and prompt templates directly from any AI agent.

Orchestrate Lingyi Wanwu AI models. Manage chat completions, embeddings, and monitor Yi model performance directly from any AI agent.

Run top-tier LLMs, image generation, and embeddings via DeepInfra's serverless infrastructure directly from your AI agent.

Access 100+ AI models through a single API. Route LLMs, generate embeddings, and execute specialized AI tasks like OCR and translation.

Access powerful LLMs, fine-tune models on your own data, and generate embeddings directly through your AI agent.

High-speed AI inference for Llama 3, DeepSeek, and MiniMax models via SambaNova's ultra-fast SN40L chips.

Generate high-quality text, multimodal, and contextualized embeddings, plus high-precision reranking for RAG workflows.