Cohere (AI Platform) MCP. Manage RAG, Embeddings, and Text Generation in One Flow
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Cohere (AI Platform) MCP Server gives your AI client direct access to Cohere's core language models. You can execute complex workflows—like generating text, classifying inputs, or generating vector embeddings—all from a natural conversation.
It lets your agent use state-of-the-art LLMs (like Command) for tasks from semantic search to document processing.
What your AI agents can do
Chat generation
Executes conversational transformations formatted by the user.
Classify inputs
Assigns text to predefined categories and returns an audit confidence score.
Generate embeddings
Converts input text into high-dimensional vector representations.
Your agent executes formatted chat transformations and retrieves structured token strings using large language models.
Your agent analyzes documents and reorders chunks based on how relevant they are to a specific search query.
Your agent takes plain text and converts it into dense numerical vectors for semantic search.
Your agent assigns text to pre-mapped labels and gives you a score showing how sure it is about the classification.
Your agent checks which models are available on your plan by listing their hashes and identifiers.
Your agent breaks down text into its smallest integer tokens, matching the specific encoding model rules.
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Supported MCP Clients
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Cohere (AI Platform) MCP Server: 7 Tools for Advanced NLP
Use these 7 tools to execute structured text operations, from vector creation and document reordering to chat completion and input classification.
019d7577chat generation
Executes conversational transformations formatted by the user.
019d7577classify inputs
Assigns text to predefined categories and returns an audit confidence score.
019d7577generate embeddings
Converts input text into high-dimensional vector representations.
019d7577generate text
Creates static text content based on provided constraints.
019d7577list models
Inspects and returns details about the available API models on your plan.
019d7577rerank documents
Structures and reorders document chunks based on their context relative to a query.
019d7577tokenize text
Segments text into its fundamental integer tokens according to the model's encoding rules.
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 (AI Platform), 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
Your AI client gets direct access to Cohere's core language models. You can run complex workflows—like generating text, classifying inputs, or creating vector embeddings—all from a natural conversation. It lets your agent use state-of-the-art LLMs (like Command) for everything from semantic search to document processing.
Your agent can execute formatted chat transformations and get structured token strings using large language models via chat_generation. You can also generate static text content based on constraints using generate_text. To improve search relevance, your agent analyzes documents and reorders chunks based on how relevant they are to a specific query using rerank_documents.
You can take plain text and convert it into dense numerical vectors for semantic search with generate_embeddings. Your agent assigns text to pre-mapped labels and gives you a score showing how sure it is about the classification using classify_inputs. You can break down raw text into its smallest integer tokens, matching the specific encoding model rules, by calling tokenize_text.
Finally, you check which models are available on your plan by listing their hashes and identifiers using list_models.
How Cohere (AI Platform) MCP Works
- 1 First, subscribe to the Cohere server and enter your API Key (either trial or production).
- 2 Second, point your AI client (Claude, Cursor, etc.) to the MCP endpoint. Your agent can then call specific tools like
generate_embeddingsorrerank_documents. - 3 Finally, your agent processes the results—whether it's a list of embeddings or a reordered document array—and continues the workflow.
The bottom line is you manage complex, multi-step generative AI workflows directly from your AI client, without writing boilerplate API code.
Who Is Cohere (AI Platform) MCP For?
The Data Scientist who needs to evaluate embedding quality and reranking performance for RAG pipelines. The AI Developer prototyping generative features. The Product Manager who needs to quickly test enterprise-grade language model capabilities. Or the Engineer auditing tokenization and model availability for complex NLP applications.
Uses generate_embeddings and rerank_documents to build and evaluate the core components of a Retrieval-Augmented Generation (RAG) system.
Tests and debugs text generation and chat completion logic using chat_generation in natural language conversation.
Prototyping new generative features by calling generate_text and classify_inputs to validate product ideas quickly.
Audits tokenization processes using tokenize_text and verifies API limits using list_models when building complex NLP applications.
What Changes When You Connect
- Build better search retrieval. Use
rerank_documentsto automatically reorder documents, making sure the most relevant context chunk hits the LLM, not just the first one. - Scale your data understanding. Generating embeddings with
generate_embeddingsturns raw text into dense vectors, letting your system find semantic matches across massive, unstructured datasets. - Control the output. Use
generate_textfor simple, static content creation orchat_generationfor complex, multi-turn conversations, giving you predictable output. - Audit your inputs.
classify_inputsdoesn't just label text; it provides a confidence score, letting you filter out low-certainty classifications before passing data downstream. - Deeply understand your model.
list_modelslets your agent check which models are active on your plan, preventing runtime errors when you scale up or change providers. - Process text at the core level.
tokenize_textbreaks text down to raw integer segments, which is critical for auditing or building highly specialized NLP pipelines.
Real-World Use Cases
Improving internal knowledge search accuracy
A company needs to build a better internal knowledge base. Instead of just searching by keywords, the agent first calls generate_embeddings on the query and the documents. Then, it uses rerank_documents to reorder the top 20 results by semantic relevance, ensuring the LLM gets the best context.
Building a customer support chatbot
A support agent needs a chatbot that handles conversations. The agent uses chat_generation to handle the multi-turn dialogue. If the conversation gets complex, it can use classify_inputs to route the query to the right department (Billing, Tech Support, etc.).
Content moderation and data pipeline validation
A data engineer is building a content pipeline. The agent first runs tokenize_text to verify the encoding structure. Then, it uses classify_inputs to filter out any text that doesn't fit the 'product description' category before generating the final content via generate_text.
Generating structured product documentation
A product team wants to prototype a new feature. The agent uses generate_text to create the initial draft copy, then uses list_models to confirm the best model for the desired complexity, ensuring the output meets the required quality standard.
Vectorizing and comparing product catalogs
An e-commerce team wants to compare two product lines. The agent runs generate_embeddings on key phrases from both lines. It then uses the resulting vectors to find the closest matches, identifying potential feature overlap for marketing materials.
The Tradeoffs
Over-relying on simple chat prompts
Telling the agent, 'Summarize this document and tell me the key points.' The agent gives a summary, but if the document is long, the key points might be mixed up or lack specific source attribution.
→
Don't just prompt. Use the pipeline: First, run generate_embeddings on the document and the query. Then, use rerank_documents to prioritize the top 5 context chunks. Finally, pass those 5 chunks to chat_generation to force the LLM to cite its sources.
Ignoring model limitations
Trying to run a massive, complex chat history through the server, only to get an error because the underlying model used by the chat function is outdated or unsupported.
→
Before running, call list_models to check the available model identifiers and ensure your agent is targeting a supported version. This prevents runtime failures and keeps your workflow stable.
Treating text as a simple string
Passing raw, uncleaned text directly to the embedding function, resulting in vectors that are noisy because the text contains mixed formatting, boilerplate, or headers.
→
Clean the text first. Use tokenize_text to validate the text's structure against the required encoding model, ensuring only pure, clean segments are passed to generate_embeddings.
When It Fits, When It Doesn't
Use this server if your workflow requires more than just a single prompt/response cycle. You need to process, validate, or structure data before the LLM gets to it. Specifically, if you need to find the most relevant document chunk, use rerank_documents. If you need to convert text into a searchable format, use generate_embeddings. If you need to validate the text's structure or know which models are available, use tokenize_text or list_models. Don't use this if your only goal is a simple, one-off text summary; use a basic API call instead. But if you need to classify the intent of that summary, then this server is necessary.
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 7 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually cleaning data and validating inputs is a huge time sink.
Today, before sending data to the LLM, you're probably running it through multiple systems: a JSON validator, a classification microservice, and then a cleanup script. You spend hours copy-pasting results between tabs, manually checking if the data structure is right before the next stage.
With the Cohere MCP Server, your agent handles the validation. Tools like `classify_inputs` categorize the text and give you a confidence score. You're not just getting a label; you're getting an auditable pass/fail gate for your data.
The Cohere (AI Platform) MCP Server gives you granular control over text generation.
You used to rely on a single 'generate' endpoint, which gave you a black box result. Now, you can explicitly separate concerns. You use `generate_embeddings` to build the search index, then `generate_text` to write the final copy, and finally `chat_generation` to polish it all up. Each step is isolated and verifiable.
It's not just about generating text. It's about running a full, auditable pipeline, where every component—from the vector to the final sentence—is controlled by the agent.
Common Questions About Cohere (AI Platform) MCP
How does the `generate_embeddings` tool work with my custom documents? +
The generate_embeddings tool takes plain strings and converts them into dense vector shapes. This process is what allows your system to find semantic matches across your documents, even if the search query uses different words.
Can I use `rerank_documents` to improve search results from a database? +
Yes. The rerank_documents tool takes initial search results (the document chunks) and reorders them based on their actual relevance to your query. It moves the most important context to the top.
Is `chat_generation` the same as `generate_text`? +
No. generate_text executes static generation for simple, foundational tasks. chat_generation handles formatted conversational transformations, meaning it's built for back-and-forth dialogue.
What is the benefit of using `tokenize_text`? +
The tokenize_text tool breaks down text into its exact integer segments. This is essential for debugging or building NLP systems that need to know the precise structural boundaries of the text.
How does `classify_inputs` handle different data sources and formats? +
It evaluates static limits by accepting text from any source. You just pass the text into the tool, and it returns the predefined label and confidence score. This makes it flexible for classifying incoming data streams.
What are the best practices for rate limiting when using `generate_embeddings`? +
We recommend batching your embedding requests to stay under API limits. If you hit a rate limit, your AI client should implement a retry logic with exponential backoff. This keeps your data pipeline running smoothly.
When should I use `list_models` instead of `generate_text`? +
list_models shows you which model hashes are available on your plan. You run this first to check capability branches before committing to a generation job, ensuring you use the right model.
Does `tokenize_text` support custom encoding schemas? +
The tool retrieves exact integer segments based on the specific Cohere encoding models. You must use the models supported by the Cohere platform; it doesn't accept arbitrary custom schemas.
Can my agent use Cohere to generate creative or technical text? +
Yes. The 'generate_text' and 'chat_generation' tools allow you to leverage Cohere's Command models. You can provide prompts for anything from copywriting to code generation, and the agent will return the synthesized token strings.
How do I perform high-dimensional vector searches with Cohere? +
Use the 'generate_embeddings' tool. Provide an array of texts, and your agent will return the precise dense vector shapes (floats). These can then be stored in a vector database like Chroma or ClickHouse for similarity matching.
Can I audit token usage before sending a long prompt? +
Absolutely. The 'tokenize_text' tool retrieves the exact structural segmentation of your text based on the specific model's dictionary. This allows you to verify token counts and manage your context window limits efficiently.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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