Cohere MCP. Build advanced RAG and conversational agents.
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Cohere. Access enterprise AI models directly from your agent. This server lets your AI client chat with Command models, generate vector embeddings, rerank documents for search, and tokenize text—all from one place.
Use it to build sophisticated RAG pipelines or conversational agents without switching between APIs or writing custom HTTP code.
What your AI agents can do
Chat
Sends a conversation to a Cohere model and returns the model's response, complete with text, citations, and any required tool calls.
Detokenize
Reconstructs text from a list of token IDs, which is useful for debugging tokenization processes.
Embed
Generates vector embeddings for input text, suitable for semantic search and similarity comparisons.
Send a chat message to a specific Cohere model and receive a full response, including citations and any required tool calls.
Convert batches of text into vector embeddings, choosing between multiple data types (float, int8, binary) for semantic search and database storage.
Take a list of documents and a query, then re-rank them using Cohere's models to bring the most relevant results to the top.
Check which Cohere models are available by listing their names, context lengths, and supported capabilities.
Estimate the token count of text using tokenize or reconstruct text from token IDs using detokenize.
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Cohere MCP Server: 6 Tools for NLP and LLM Interaction
These tools let your agent perform complex NLP tasks, including generating vectors, chatting with advanced models, and reordering search results.
019d8427chat
Sends a conversation to a Cohere model and returns the model's response, complete with text, citations, and any required tool calls.
019d8427detokenize
Reconstructs text from a list of token IDs, which is useful for debugging tokenization processes.
019d8427embed
Generates vector embeddings for input text, suitable for semantic search and similarity comparisons.
019d8427list models
Lists every available Cohere model, providing details on context length, name, and capabilities.
019d8427rerank
Ranks a list of documents by relevance to a specific query, returning the top results with a relevance score.
019d8427tokenize
Breaks text into token IDs and strings, helping you estimate token counts before sending it to a model.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Cohere, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
You're hooking up your AI client to Cohere, so you've got access to enterprise-grade AI models right in your agent. This server lets your agent chat with Command models, generate vector embeddings, re-rank search results, and handle text tokenization—all from one place. It lets you build complicated RAG pipelines or conversational agents without having to jump between APIs or write custom HTTP code.
chat: Send a conversation to a Cohere model and get the model's full response, which includes text, citations, and any tool calls it needs to make.
embed: You can turn batches of text into vector embeddings. You'll get to pick the data type—float, int8, or binary—which is key for semantic search and storing data in your database.
rerank: When you search, don't just trust vector distance. Use this to rank a list of documents against a specific query, bringing the most relevant results right to the top, and it gives you a relevance score for each one.
list_models: Want to know what Cohere models are available? This tool lists every model, giving you its name, context length, and what it can do.
tokenize and detokenize: These handle your text count and debugging. tokenize breaks text into token IDs and strings, letting you estimate how many tokens you're sending. detokenize reconstructs text from a list of token IDs, which is useful for debugging tokenization processes.
How Cohere MCP Works
- 1 Subscribe to this server and enter your Cohere API Key.
- 2 Your AI client uses the structured tools (like
embedorrerank) in a natural conversation or code block. - 3 The server executes the API call, processes the Cohere response, and feeds the structured data back to your client.
The bottom line is you get all of Cohere’s advanced NLP tools wrapped up so your AI agent can use them without you having to write any API connection code.
Who Is Cohere MCP For?
The ML Engineer who needs to prototype a full RAG pipeline fast. The Search Team needing to reliably improve search relevance. The Developer building complex, multi-step agents. This server handles the messy API plumbing so you can focus on the logic.
Generates embeddings for different data types (float, int8, binary) and compares model capabilities using list_models to build knowledge bases.
Uses rerank to improve search quality and uses embed to build the core vector index for document retrieval.
Integrates the chat tool to power conversational features, ensuring the agent can answer questions and cite its sources.
What Changes When You Connect
- Semantic Search: Use the
embedtool to turn documents into vectors. This lets you compare text meaning, not just keyword matching, for your search index. - Improved Search Quality: Don't rely solely on vector distance. Run the
reranktool to reorder search results, ensuring the absolute best-matching documents hit the user first. - Full Conversational Context: The
chattool lets your agent talk like a professional LLM. It sends messages to Command models and provides citations so users know where the answers came from. - Model Visibility: Use
list_modelsto know exactly which models you can use. You can compare capabilities and context lengths before writing a single line of code. - API Debugging: Use
tokenizeanddetokenizeto estimate token usage and verify text processing. This saves you from running into unexpected context length errors. - Flexibility: You don't have to swap APIs. Everything—chat, embed, rerank—runs through this single, unified server.
Real-World Use Cases
Building a Q&A system for internal docs
The problem: You have a massive internal knowledge base and need to build a Q&A system. The solution: Your agent first uses list_models to check for the best embedding type. Then, it uses embed on all document chunks. Finally, when a user asks a question, the agent uses rerank on the top results to surface the single most accurate answer source.
Creating a research assistant for academic papers
The problem: A researcher needs to chat with dozens of papers without manually uploading them. The solution: The agent uses chat with a large context window. It handles the conversation and uses citations to prove its answers, making the assistant trustworthy.
Analyzing data structure for system integration
The problem: You're integrating a new data source and need to know its structure and limits. The solution: You use tokenize first to check the character limits, then list_models to see if the model supports the required context length. This prevents runtime errors.
Comparing multiple document sets for similarity
The problem: You have two separate databases and need to know which documents are semantically similar. The solution: You feed both sets of texts into the embed tool, generating vectors. You can then compare these vectors to find the closest matches across different data silos.
Fixing inconsistent token counting
The problem: Your app sometimes fails because the model thinks the prompt is too long. The solution: You use tokenize to get an accurate token count, and then detokenize to confirm that the text you plan to send hasn't been corrupted during processing.
The Tradeoffs
Calling APIs individually
Calling the Cohere chat endpoint, then separately calling the embed endpoint, and finally calling the rerank endpoint. This requires writing three different API wrappers in your code.
→
Use the Cohere MCP Server. Your agent handles the sequence. You just call chat (for conversation) or rerank (for search). The server manages the plumbing.
Assuming context length
Sending a massive prompt to the model hoping it fits, only to get a vague 'context too long' error at runtime.
→
Run the tokenize tool first. It tells you the exact token count, letting you trim your prompt or break up your documents before sending it anywhere.
Using only keyword search
Relying on standard database LIKE %query% queries which miss documents that are conceptually similar but don't share keywords.
→
Use the embed tool to generate vectors for your documents. This converts text into a mathematical space where similarity means conceptual closeness, not just word matching.
When It Fits, When It Doesn't
Use this server if you need to build a multi-step AI workflow that involves text understanding, data storage, and conversation. You need to go from raw text to a definitive, actionable answer.
Don't use this if you just need a simple API wrapper for one endpoint. If your entire process is just 'send text, get text,' you might be able to use a simpler tool. But if you need to combine chat, embedding, and reranking, this is the right place. It gives you the full stack.
If your primary goal is just data storage (e.g., pure vector writes), consider a dedicated vector database client instead. But if you need the intelligence layer on top of that storage, stick with Cohere's full toolset here.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cohere. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually connecting Cohere APIs is a pain.
Right now, if you want your agent to use Cohere's advanced features, you're writing boilerplate code for every single endpoint. You write the chat call, then you write the embedding call, then you write the reranking call. It’s a mess of API keys, headers, and error handling that takes all your time.
With the Cohere MCP Server, you connect once. Your agent treats `chat`, `embed`, and `rerank` like native functions. You just call the tool name, and the server handles the whole API handshake. You focus on the logic, not the plumbing.
Cohere MCP Server: Chat and Embeddings in One Step
Before this server, if you wanted to build a conversational agent that could cite its sources, you had to manually chain the model call with the embedding and retrieval logic. It was complex, brittle, and required multiple function calls.
Now, the agent manages the whole process. You send a message, and the `chat` tool handles the conversation. If the model needs to look up information, it can use the underlying embedding logic—all transparently to you.
Common Questions About Cohere MCP
How do I get a Cohere API Key? +
Log in to the Cohere Dashboard, go to API Keys and click Create API Key. Copy the key immediately — it starts with a random string and won't be shown again. Free tier includes trial access with rate limits.
What models are available? +
Use the list_models tool to see all available Cohere models. Key models include command-r-plus (most capable, 128K context), command-r (efficient, 128K context), command-r7b (lightweight, 128K context), embed-v4 (embeddings) and rerank-v3.5 (reranking).
Can I send multi-turn conversations? +
Yes! Pass a messages array with alternating 'user', 'assistant' and 'system' roles. Each message has a 'role' and 'content' field. Command models support function calling and will return tool_calls when appropriate.
What is reranking and when should I use it? +
Reranking reorders a set of documents by their relevance to a query. Use it after an initial search to improve result quality. The rerank tool takes a query, list of documents and returns them ranked by relevance score. Cohere's rerank models are industry-leading for search applications.
How do I use the `embed` tool to generate vector embeddings? +
The embed tool generates vector embeddings for any given text. You specify the model ID and the text(s) array, along with an input type like 'search_document'. This is critical for semantic search and comparing text similarity.
What is `tokenize` and why should I use it before chatting? +
tokenize counts the tokens in your text. You send the text you plan to use, and it returns the token IDs and strings. This helps you estimate the token count before hitting the chat limit or managing costs.
How does the `chat` tool handle tool calls or function calling? +
The chat tool handles function calling by allowing you to define a tools array. You send the message and the tool definitions; the model responds with text, citations, and a structured tool call if appropriate.
What is the difference between `embed` and `rerank`? +
embed creates dense vector representations of text for similarity search. rerank takes a query and a list of documents and returns a ranked list of those documents based on relevance score.
Multi-server workflows that include Cohere MCP
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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