Cohere (Embed & Rerank) MCP. Measure semantic relevance and structure context data.
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Cohere (Embed & Rerank) MCP Server provides direct access to high-performance text embeddings, semantic document reranking, and AI classification. It lets your agent generate dense vector shapes for knowledge retrieval, structure context chunks by priority, and execute complex conversational transformations.
Use it to power RAG pipelines and run advanced text analysis directly from any AI client.
What your AI agents can do
Chat completion
Executes conversation transformations that follow a specific, formatted structure.
Classify texts
Determines which predefined class a given string belongs to and evaluates static limits.
Embed texts
Generates dense vector shapes that map the meaning of plain text strings.
Pass plain text and receive dense vector representations (floats) used for measuring semantic similarity.
Take a set of documents and a query, and receive a prioritized list of chunks based on relevance score.
Execute multi-step chat commands using Cohere's specified model parameters and conversational format.
Pass text and a set of defined labels, receiving the predicted category and a confidence score.
Send a text string and receive the precise structural breakdown and total token count for auditing purposes.
List all Cohere models and their hashes to confirm API availability for your current plan.
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Cohere (Embed & Rerank) MCP Server: 6 Tools for AI Context
Use these tools to generate vector embeddings, rank documents by relevance, and perform structured text analysis for advanced AI workflows.
019d7577chat completion
Executes conversation transformations that follow a specific, formatted structure.
019d7577classify texts
Determines which predefined class a given string belongs to and evaluates static limits.
019d7577embed texts
Generates dense vector shapes that map the meaning of plain text strings.
019d7577list models
Lists the internal properties and hashes of all available Cohere models for your account.
019d7577rerank documents
Sorts multiple documents and context chunks by their relevance to a specific query.
019d7577tokenize text
Breaks down a text string into its exact structural tokens for counting and auditing.
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.
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Make Your AI Do More
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- Works with Claude, ChatGPT, Cursor, and more
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What you can do with this MCP connector
This Cohere server gives your agent direct access to vector embeddings, document reranking, and AI classification. You can generate dense vector shapes for plain text using embed_texts, which lets your agent measure semantic similarity. You'll get a prioritized list of context chunks by passing a set of documents and a query to rerank_documents.
You can run multi-step chat commands using Cohere's specified model parameters and conversational format with chat_completion. You'll determine which predefined class a given string belongs to and check static limits by passing text to classify_texts. You can send a text string to tokenize_text and get its precise structural breakdown and total token count for auditing.
To check what's available, you'll list all Cohere models and their hashes by calling list_models.
How Cohere (Embed & Rerank) MCP Works
- 1 Subscribe to the server and enter your Cohere API key (use a Trial or Production key).
- 2 Your AI client calls a specific tool (e.g.,
embed_texts) and sends the input data. - 3 The server executes the Cohere API call and returns the structured output (vectors, scores, or text) directly to your agent.
The bottom line is, your agent uses the standard MCP call structure to execute complex Cohere operations without needing to manage API keys or network logic.
Who Is Cohere (Embed & Rerank) MCP For?
This is for the ML Engineer who builds RAG pipelines and needs to test embedding/reranking logic without writing boilerplate code. It’s for the Data Scientist who needs real-time semantic matching accuracy scores. It’s for the Product Team needing to prototype search or classification features quickly.
Builds and debugs RAG pipelines by calling embed_texts and rerank_documents to test vector logic and context scoring.
Evaluates the semantic matching accuracy and classification confidence using classify_texts and embed_texts on live data.
Prototyping search or retrieval features by calling rerank_documents to demonstrate improved search relevance to stakeholders.
What Changes When You Connect
- Improve RAG accuracy by using
rerank_documents. Instead of relying on basic keyword matching, the agent scores and reorders document chunks, ensuring the most relevant context hits the LLM. - Power semantic search by calling
embed_texts. This tool converts simple text into high-dimensional vector floats, allowing your agent to find documents based on meaning, not just keywords. - Audit model usage and costs by using
tokenize_text. You get the exact structural segmentation and token count, letting you know exactly how much context you’re sending. - Build stateful agents using
chat_completion. This tool lets your agent handle complex, multi-turn conversations while respecting Cohere's generation limits. - Validate data integrity with
list_models. Check the internal properties and hashes of all available Cohere models to ensure your agent can use the right version. - Categorize inputs instantly using
classify_texts. The agent passes text to this tool, which returns a predicted label and a confidence score for immediate data validation.
Real-World Use Cases
Improving internal knowledge search
A customer service agent needs to find the best answer from a massive internal wiki. Instead of just searching by keywords, the agent calls embed_texts on the query and all wiki articles. It then runs rerank_documents on the results, ensuring the top three retrieved chunks are the most semantically relevant before generating a final answer.
Validating document structure before processing
A developer receives a large data file and needs to know how many tokens it contains before sending it to the LLM. The agent runs tokenize_text first. This provides the exact token count, preventing costly API overruns and ensuring the input fits the model's context window.
Routing user intent to specific business processes
A support bot gets a message: 'I need to change my billing address.' The agent calls classify_texts to categorize the intent. If the score hits 'Billing', the agent routes the conversation to a specialized workflow, skipping general chat processing.
Complex, multi-step agent workflows
A research agent needs to summarize a report and then categorize it. It uses chat_completion to draft the summary, and then immediately passes the summary text to classify_texts to assign a formal risk level (e.g., High, Medium, Low). This sequence ensures the output is both narrative and structured.
The Tradeoffs
Assuming keyword search is enough
The agent simply searches the knowledge base using keywords, returning documents that are technically related but miss the core meaning. The user gets generic, unhelpful answers.
→
Instead, use embed_texts on the user query, then call rerank_documents with the resulting vectors. This forces the system to rank documents by semantic closeness, not just word overlap.
Ignoring token limits
The agent collects all available documentation chunks and sends them all to the LLM in one go, resulting in an API error because the input exceeds the model's context window.
→
Always run tokenize_text first. Use the reported token count to cap your input, ensuring your retrieval process only sends a manageable number of chunks.
Treating classification as a single pass
The agent relies on the LLM to 'guess' the category without explicit instructions, leading to inconsistent or hallucinated labels.
→
Use classify_texts. This tool forces the model to evaluate against a predefined list of labels and provides a measurable confidence score, making the output reliable.
When It Fits, When It Doesn't
Use this MCP Server if your application requires deep understanding of text meaning or structured data extraction. You need to measure semantic relevance, not just keyword overlap. Specifically, if you are building a RAG pipeline, you must use embed_texts and rerank_documents together. You need structured outputs? Use classify_texts. You're just building a simple chat bot that talks to a single endpoint? You might not need the full suite. Don't use this if your goal is just basic data storage; use a standard database instead. This server is for complex, high-accuracy AI logic.
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
Retrieval-Augmented Generation shouldn't just rely on basic database lookups.
Today, when a user asks a question, the common workflow is: Query -> Database Search (keywords) -> Retrieve Documents -> Send to LLM. This process fails when the user's phrasing differs from the document's language, or when the knowledge base is huge. The LLM gets too much noise.
With the Cohere MCP Server, the workflow changes. The agent first calls `embed_texts` to turn the query into a vector. Then, it uses `rerank_documents` to score all candidates against that vector. The LLM only gets the top 3, most relevant, context chunks. The answer is better, and the process is measurable.
Cohere (Embed & Rerank) MCP Server: Structured AI Outputs
Before, getting a category label required writing complex prompts that sometimes failed or gave ambiguous answers. You'd have to manually check the LLM output to see if the label was plausible.
Now, you call `classify_texts`. The tool handles the validation, giving you a definitive category and a concrete confidence score. It’s a reliable, measurable step that locks down the output structure.
Common Questions About Cohere (Embed & Rerank) MCP
How do I use `embed_texts` in my agent? +
embed_texts takes an array of plain strings and returns dense vector shapes. You pass the text, and the tool gives you the floats you need for similarity calculations.
What is the difference between `embed_texts` and `rerank_documents`? +
Embedding creates the vectors for all texts. Reranking takes those vectors and a query, and it calculates which specific documents are closest to the query's meaning, giving you a ranked list.
Can I check my API usage with `list_models`? +
Yes, list_models enumerates the available Cohere models and their hashes. This lets you confirm your agent is using a model allowed by your current API plan.
How does `tokenize_text` help with token limits? +
tokenize_text provides the exact structural segmentation and token count. This is critical because it tells you the precise number of tokens before the LLM context window fills up.
Does `chat_completion` handle multi-turn conversations? +
Yes, chat_completion executes formatted conversational transformations. It manages the conversational state and respects the generation limits for multi-step dialogues.
How do I use `classify_texts` to categorize user input? +
You call classify_texts with the input string and the predefined labels. This function returns the classification and a confidence score, letting you know how sure the model is about the category.
What is the best way to audit my context length using `tokenize_text`? +
Pass the full text you plan to send to tokenize_text. It gives you the exact integer segmentation, which is crucial for checking if your input fits within the model's token limit.
Does `rerank_documents` handle document chunk overlap? +
Yes, rerank_documents takes an array of document chunks and a query. It structures them based on relevance to the query, regardless of whether those chunks overlap or not.
Can my agent improve my RAG system's accuracy using Cohere? +
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.
How do I test text classification via the agent? +
Use the 'classify_texts' tool. Provide your input strings and a few-shot JSON array of examples (text and label). The agent will return the predicted categories along with confidence scores from the Cohere engine.
What is the difference between Trial and Production keys? +
Trial keys are free for development but have strict rate limits (approx. 1,000 calls per month). Production keys remove these limits but require a paid plan. Both types work seamlessly with this server.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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