# Cohere MCP for AI Agents AI Agent Connect

> Cohere MCP lets your AI agent talk directly to Cohere's models. You can send prompts to Command models, generate embeddings for vector databases, rerank search results, and handle tokenization without writing any custom API code. It's the fastest way to put enterprise-grade NLP into your agent's toolkit.

## Overview
- **Category:** ai-frontier
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_R4ERIik35jE1vo5hRbvNHBrwGzLbWOtl3sZQNmPU/ai-agent-connect
- **Tags:** llm, embeddings, reranking, natural-language-processing, tokenization, chat-api

## Description

You're tired of jumping between different API docs just to get a search result to actually make sense. This Connector changes that by letting your agent handle the heavy lifting of model orchestration directly. Instead of writing custom scripts to send requests, your agent can now pull embeddings for your vector database, rerank a messy list of documents to find the best match, or chat with the Command models to get grounded responses. It turns your agent into a high-level orchestrator. When you find this in the Vinkius catalog, you're basically giving your agent a direct line to enterprise-grade NLP. You can check available models on the fly, count tokens before you hit a limit, or turn raw text into vectors for a semantic search engine. It's about getting the right answer from the right model without the friction of manual API management. You don't have to worry about the specific formatting for different embedding types like float or binary because the agent handles that. If you're building a search pipeline, you know how frustrating it is when the first few results are irrelevant. This Connector lets your agent fix that by scoring and sorting those results by relevance before it ever presents them to you. It takes the manual work out of the equation, letting you focus on the actual logic of your application rather than the plumbing of the API.

## Tools

### detokenize
Convert a list of token IDs back into plain text for debugging and verification. This helps you see exactly what your agent is processing.

### chat
Send a message to a Cohere Command model to get a response with citations and tool call support. This is useful for high-quality conversational tasks.

### list_models
See a full list of Cohere models, their capabilities, and their context lengths. Use this to discover which models fit your specific needs.

### rerank
Reorder a list of documents based on how well they match a specific search query. This ensures your agent gets the most relevant facts first.

### tokenize
Break down text into token IDs to estimate costs and limits before sending data. This helps you manage your token budget effectively.

### embed
Create vector embeddings for various tasks like search, classification, or clustering. This is the standard way to prepare data for a vector database.

## Prompt Examples

**Prompt:** 
```
How many tokens are in this paragraph?
```

**Response:** 
```
Here is the token count for that text:

| Metric | Value |
| :--- | :--- |
| **Total Tokens** | 42 |
| **Token IDs** | [123, 456, 789...] |

It's well within the limit for Command-R.
```

**Prompt:** 
```
Rerank these 3 sentences for the query 'climate change': ['The sun is hot.', 'Global temperatures are rising.', 'I like pizza.']
```

**Response:** 
```
Here are the reranked results:

1. **'Global temperatures are rising.'** (Score: 0.98)
2. **'The sun is hot.'** (Score: 0.45)
3. **'I like pizza.'** (Score: 0.01)

The transformer and neural network documents are most relevant to ML models.
```

**Prompt:** 
```
Generate embeddings for 'How do I bake a cake?' using the embed-v4 model.
```

**Response:** 
```
I've generated the 1024-dimensional vector for that query. You can now use this vector in your database to find similar recipes.
```

## Capabilities

### Chat with Command models
Send text to Command-R+ and get responses with citations.

### Rerank search results
Sort documents by relevance to a specific query.

### Generate vector embeddings
Create numerical representations for semantic search.

### List all available models
See what models exist and their context limits.

### Count and manage tokens
Get token IDs and counts for text inputs.

### Reconstruct text from IDs
Turn token IDs back into readable strings.

## Use Cases

### Refining a RAG pipeline
A developer asks the agent to rerank 50 search results to find the top 5 most relevant facts.

### Building a semantic search
An engineer uses embed to turn a product catalog into vectors for a find similar items feature.

### Multi-model orchestration
A user asks the agent to list_models to see which Command version has the best context for a long document.

### Token budget tracking
A dev uses tokenize to check if a long user prompt will fit into the model's context window.

## Benefits

- Skip the boilerplate: Use chat to talk to Command models without writing custom HTTP request code.
- Improve search accuracy: Use rerank to sort search results by relevance, making your RAG system much more reliable.
- Build better vector databases: Use embed to quickly generate float, int8, or binary embeddings for semantic search.
- Manage your limits: Use tokenize to see exactly how many tokens a piece of text uses before you send it to a model.
- Discover capabilities: Use list_models to see exactly which models are available and what their context lengths are.
- Debug your data: Use detokenize to see what your agent is actually seeing when it processes token IDs.

## How It Works

The bottom line is you get a direct pipeline to Cohere's models without writing a single line of HTTP code.

1. Subscribe to the Cohere MCP in the Vinkius marketplace.
2. Add your Cohere API Key to the configuration.
3. Tell your agent to perform a task like reranking documents or generating embeddings.

## Frequently Asked Questions

**What does the Cohere MCP do for my AI agent?**
It gives your agent the ability to use Cohere's models for reranking, embeddings, and chat. It acts as a bridge so your agent can perform complex NLP tasks without needing custom code.

**Can I use Cohere MCP to improve my search results?**
Yes, it's great for that. You can use it to rerank a list of documents, ensuring your agent only sees the most relevant information first.

**How do I get embeddings for my database using Cohere?**
You can have your agent use the embed tool to turn your text into vectors. This works for search, clustering, and classification tasks.

**Does the Cohere MCP support the Command models?**
Yes, it lets your agent send messages to Command-R, Command-R+, and Command-R7B to get high-quality, cited responses.

**Can I check my token counts with Cohere MCP?**
Yes, you can use the tokenize tool to see exactly how many tokens a piece of text uses before you send it to a model.

**Is Cohere MCP good for RAG systems?**
It's a top choice for RAG because it handles the two hardest parts: generating high-quality embeddings and reranking the retrieved results.

**How do I get a Cohere API Key?**
Log in to the [**Cohere Dashboard**](https://dashboard.cohere.com/api-keys), 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.