Cohere (Embed & Rerank)

Cohere (Embed & Rerank) MCP Connector for Claude

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

5 tools Official Updated Jun 28, 2026 Official Vinkius Partner

Connect your Cohere account to any AI agent and take full control of your enterprise AI and RAG workflows through natural conversation.

What you can do

  • Text Embeddings — Generate precise dense vector shapes for plain strings to power semantic search and knowledge retrieval
  • Semantic Reranking — Structure contextual chunks by priority ordering documents against specific queries for improved RAG accuracy
  • Conversational AI — Execute formatted conversational transformations using Cohere's generation limits and state-of-the-art LLMs
  • Text Classification — Categorize inputs into predefined labels using few-shot training blocks and extract confidence scores
  • Tokenization — Retrieve exact structural segmentation of NLP contexts to audit token counts and model dictionaries
  • Model Registry — Enumerate available Cohere models and hashes to verify API availability based on your plan

How it works

  1. Subscribe to this server
  2. Enter your Cohere API Key (Trial or Production key from the Dashboard)
  3. Start optimizing your RAG pipelines from Claude, Cursor, or any MCP-compatible client

Who is this for?

  • AI Developers — test and debug embedding and reranking logic without writing boilerplate code
  • Data Scientists — evaluate semantic matching accuracy and text classification confidence in real-time
  • Product Teams — quickly prototype search and retrieval features using enterprise-grade AI models
  • LLM Engineers — audit tokenization and model availability for complex conversational workflows
embeddingssemantic-searchvector-representationnatural-language-processingragtext-analysis

5 tools expose this connector's capabilities to your AI agent.

list_models

List all available Cohere models on your plan

tokenize_text

Tokenize text into token IDs for a specific model

embed_texts

Generate dense vector embeddings from text using Cohere models

rerank_documents

Rerank documents by semantic relevance to a query

chat_completion

Generate conversational text responses using Cohere chat models

See how to talk to your AI agent using Cohere (Embed & Rerank).

Generate embeddings for these texts: ['Hello world', 'Artificial Intelligence']

Embeddings generated! I've retrieved the dense vector representations for both strings. You can now use these floats for semantic search or similarity calculations.

Rerank these documents for query 'Best pizza in NY': ['Pizza hut review', 'Joe's Pizza is the local favorite']

Reranking complete! 'Joe's Pizza is the local favorite' has been moved to rank 0 with a high relevance score. 'Pizza hut review' is now at rank 1.

How many tokens are in the text: 'The quick brown fox jumps over the lazy dog'?

That sentence contains 9 tokens according to the Cohere tokenizer. I can provide the exact integer array mapping these tokens if you'd like.

Yes. The 'rerank_documents' tool is specifically designed for this. Provide a query and a list of documents, and Cohere will reorder them based on semantic relevance, ensuring the most accurate context is fed to your LLM.

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