Cohere MCP. Manage Embeddings, Chat, and Reranking in One Flow
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Cohere provides an API gateway for enterprise-grade AI models, letting your agent handle everything from advanced chat conversations and document reranking to generating vector embeddings and precise text tokenization.
It's a single connection point for complex NLP pipelines.
What your AI agents can do
Chat
Sends a message to a Cohere model, returning text responses along with necessary citations and tool call suggestions.
Detokenize
Reconstructs readable text from an array of token IDs, which helps verify the integrity of tokenization processes.
Embed
Creates vector embeddings for given texts using a specified model and input type, useful for semantic comparisons.
Send complex messages to advanced models, receiving responses that include source citations and function call support.
Create numerical representations of text for semantic search or similarity comparisons using various input types.
Take a query and a set of documents, then reorder them by calculated relevance score to improve retrieval accuracy.
List all available Cohere models, showing their names, context length limits, and capabilities for planning.
Break down text into tokens or reconstruct text from token IDs to accurately predict API costs and manage input size.
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Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Cohere Tools: 6 Utilities for NLP Pipelines
These tools allow you to manage the entire lifecycle of natural language data, from initial text input through advanced embedding generation and final model chat interactions.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Cohere on Vinkius019d8427chat
Sends a message to a Cohere model, returning text responses along with necessary citations and tool call suggestions.
019d8427detokenize
Reconstructs readable text from an array of token IDs, which helps verify the integrity of tokenization processes.
019d8427embed
Creates vector embeddings for given texts using a specified model and input type, useful for semantic comparisons.
019d8427list models
Retrieves names, context lengths, and capabilities of all models Cohere offers, allowing you to choose the right tool for the job.
019d8427rerank
Scores a set of documents against a query text and returns them in order of relevance, with confidence scores.
019d8427tokenize
Converts raw text into token IDs or vice versa, which is critical for accurately measuring token usage before sending prompts.
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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.
Manual document processing requires too much context switching.
Today, if you want to search a company's knowledge base, you often have to copy articles into one system, run them through an embedding generator on a separate dashboard, then take those vectors and manually paste them into your vector database. You spend hours moving data between three different dashboards just to get the right answers.
With this MCP, that manual process disappears. Your agent handles it all: you ask a question, the system uses embed to generate vectors for both the query and the knowledge base chunks, then rerank scores them instantly. The result is an accurate answer with source links.
Using the chat tool provides conversational answers with citations.
The old way was getting a monolithic block of text that sounded plausible but might be wrong or vague. You’d have to manually verify every claim against the source material, wasting time and risking hallucination.
Now, when your agent chats with the model, it doesn't just answer; it provides citations for everything it says. That changes the game completely; you get verifiable answers built right into the workflow.
What you can do with this MCP connector
This MCP connects your workflow directly to Cohere’s powerful suite of natural language processing tools. You can use it to manage entire information retrieval cycles—from taking raw user input, running that through the model discovery tool to check available models, generating semantic embeddings, and then reranking documents against a specific query.
Need to estimate token limits before sending a massive prompt? The tokenization tool handles that quickly.
It's built for pipelines: if you’re building an application where data moves from one state to another—for instance, taking raw text, embedding it, and then passing those vectors into a database for retrieval—this MCP lets your agent orchestrate all of that without switching APIs. When you combine this with other specialized services in the Vinkius catalog, you can chain multiple operations together through one AI agent, building automations that span different platforms.
This setup means you stop writing dedicated HTTP calls just to interact with Cohere. Your AI client acts as a single orchestration layer for all your NLP needs.
019d8427-e006-726d-9934-e74c17758f9a 
How Cohere MCP Works
- 1 Subscribe to this MCP in Vinkius and provide your required Cohere API Key.
- 2 Connect your agent (e.g., Claude, Cursor) once from that single client connection.
- 3 Your agent can then execute the various NLP operations—like generating embeddings or reranking documents—as part of a larger workflow.
The bottom line is you get to manage multiple advanced AI models and data tasks through one standard, predictable API interface.
Who Is Cohere MCP For?
ML Engineers who spend too much time manually writing wrapper code for different endpoints. Search Scientists building RAG pipelines that need document relevance scoring. Developers trying to build robust NLP tools without getting bogged down in complex, multi-step API calls.
Uses the tool to discover available models and generate embeddings for different data types (float, int8) within a single development session.
Reranks documents based on query relevance and tokenizes text before building search indexes, ensuring high precision retrieval.
Builds chat interfaces that require source citations or needs to estimate the exact token count for complex prompts.
What Changes When You Connect
- Get accurate source citations directly from the chat tool. When your agent answers a question, it doesn't just guess; it tells you where its information came from.
- Build semantic search indexes efficiently by using the embed tool to turn documents and queries into comparable vector representations.
- Stop relying on simple keyword matching for search results. The rerank tool reorders retrieved documents based on deep relevance scores, making your search feel smarter.
- Accurately predict token usage before running a prompt. Use the tokenize or detokenize tools to test input sizes and prevent costly API overruns.
- Streamline model selection by listing all available Cohere models using list_models; you instantly know which models support embeddings versus chat.
Real-World Use Cases
Building a Q&A bot with source validation
A user asks, 'What was the company's revenue last year?' The agent uses the chat tool to get an answer and simultaneously pulls citations showing exactly which internal document chunk provided that specific figure.
Improving a complex knowledge base search
Instead of just searching keywords, the system first runs embed on the query. It then retrieves 20 candidate documents and uses rerank to cut that list down to the top 5 most relevant pieces for the user.
Debugging a large prompt payload
A developer needs to send a long document chunk but isn't sure if it will exceed the token limit. They use tokenize on the text first, guaranteeing they stay under budget before calling chat.
Creating multi-step content analysis
The agent receives an article, uses embed to create vectors for the article and then passes those vectors through a second MCP's retrieval tool for comparison against other stored data.
The Tradeoffs
Calling APIs directly for every step
Writing separate Python functions to call embed, then another function for rerank, and a third for chat. This creates brittle code that's hard to maintain.
→ Instead, let your agent orchestrate the flow. Use the single connection point provided by Vinkius to chain these tools together automatically: first run embed on the query; second run list_models to confirm capability; finally, pass the results into chat.
Ignoring token limits
Sending a 10,000-word document chunk to an LLM without checking the model's context window first. The API call fails or truncates data.
→ Always run tokenize on your input text before making any chat call. This guarantees you know the exact token count and prevents unexpected failures.
Treating embeddings as just text
Storing raw text results from an embed tool into a simple database column, losing the vector dimension needed for similarity search.
→ Use the output of the embed tool directly to populate a dedicated vector store. The resulting vectors allow your agent to perform true semantic matching.
When It Fits, When It Doesn't
You should use this MCP if your task requires more than just simple text generation; specifically, if you need to improve search results, compare documents semantically, or manage complex information pipelines. Use it when the data flow involves distinct stages: source document -> vector embedding -> relevance scoring -> final chat response. Don't use this MCP if all you need is a single-turn question answer with no citation requirements, as that might be better handled by simpler LLM wrappers. If your core task is just counting words or simple text cleaning, stick to basic string manipulation instead of running the tokenize tool.
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.
When using the `embed` tool, how do I choose the right input type for my vectors? +
You must specify the purpose when calling embed. Use 'search_document' to index general text for similarity search. Alternatively, use 'classification' if your goal is grouping or labeling documents based on predefined categories.
How do I estimate my token count before running a long chat with the `chat` tool? +
Run the tokenize tool first. It returns the precise list of token IDs and strings, letting you accurately predict how many tokens your prompt will use for cost estimation or length checks.
When using the `rerank` tool, how do I ensure I only get the top results? +
You set the optional top_n parameter when running rerank. This limits the output to return exactly N documents, which saves tokens and keeps your search result display clean.
Does the `chat` tool support structured responses or function calling? +
Yes, the chat tool handles explicit tool call functionality. It returns not only conversational text but also detailed data about any potential functions it determines are necessary to execute.
Multi-server workflows that include Cohere MCP
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.