# Cohere (AI Platform) MCP for AI Agents AI Agent Connect > Cohere (AI Platform) MCP gives your AI agent direct access to Cohere's enterprise-grade language models. It lets your agent generate high-quality text, create dense vector embeddings for semantic search, and rerank documents to improve RAG accuracy. It's the bridge between your local agent and Cohere's production-grade NLP capabilities. ## Overview - **Category:** ai-frontier - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_fA1l2oqJxpD7QL48YxxEDHfZLGLU7FMkmFVJY7Ft/ai-agent-connect - **Tags:** llm, generative-ai, natural-language-processing, chat-completion, reranking, ai-api ## Description Connecting your agent to the Cohere platform changes how you handle heavy-duty language processing. Instead of manually moving data between different services to build a search tool or a chat interface, your agent does the heavy lifting for you. You can ask it to turn a messy pile of documents into a structured search index by creating high-dimensional vectors or have it prioritize the best results for a specific user query to improve accuracy. It handles the technical details of turning strings into integer segments and selecting the right model for your specific needs so you can focus on the actual output. You can run these operations in a single flow, moving from raw data to searchable knowledge without jumping between different dashboards. This is a big part of why the Vinkius catalog is so useful for teams who need to move fast without managing a dozen different API keys and connections manually. You get a unified way to interact with enterprise-grade models that actually understand context, whether you're building a customer support bot or a complex internal knowledge base. It takes the friction out of NLP by making these advanced capabilities feel like a natural part of your agent's toolkit, letting you build more reliable systems with less overhead. ## Tools ### generate_embeddings Create dense vector representations from your text. This allows your agent to build and query a semantic search index. ### rerank_documents Reorder a list of documents based on how well they match a specific query. It helps fix the noise in standard search results. ### chat_completion Get human-like responses from Cohere's chat models. Use this for building conversational interfaces or summarizing long text. ### tokenize_text Turn strings into specific integer IDs for a chosen model. This is essential for understanding how your data fits into a model's brain. ### list_models See every model available on your specific Cohere plan. Use this to check if you have access to the latest releases. ## Prompt Examples **Prompt:** ``` Generate embeddings for these 3 product descriptions ``` **Response:** ``` Embeddings generated! I have the high-dimensional vector representations for all 3 descriptions. They are ready to be indexed for your semantic search feature. ``` **Prompt:** ``` Rerank these search results for 'AI implementation guide': [result_1, result_2, result_3] ``` **Response:** ``` Reranking complete! I've reordered the results based on their semantic relevance to your query. Result_3 is now the top recommendation with a 98% relevance score. ``` **Prompt:** ``` Generate a summary of this article: [article text] ``` **Response:** ``` Generating summary... I've synthesized the main points of the article using the Command model. The summary highlights the key findings and conclusions in a concise format. ``` ## Capabilities ### Generate high-dimensional vectors Create embeddings from plain text to power semantic search and similarity matching. ### Prioritize search results Use reranking to put the most relevant documents at the top of a search result list. ### Produce conversational text Get high-quality chat completions for customer-facing bots and conversational apps. ### Identify text categories Categorize and label incoming text into predefined buckets automatically. ### Map text to IDs Convert strings into precise token segments for model compatibility and count tracking. ### Check model availability See which specific models your current plan supports in real-time. ## Use Cases ### Evaluating chunking strategies A data scientist needs to see if a new chunking strategy improves search. They ask the agent to reorder a set of results and report the top 3. ### Calculating token limits A product manager wants to see how many tokens a 50-page PDF uses. They ask the agent to convert the text and give a total count. ### Building a product search A dev needs to build a product search. They ask the agent to create vector representations for 100 descriptions and save them. ### Testing brand tone A support lead wants to see if a model can handle a specific brand tone. They ask the agent to generate several responses for common FAQs. ## Benefits - Improve search accuracy by automatically prioritizing the most relevant documents before they reach your user. - Build faster search features by turning your entire knowledge base into a searchable vector space. - Reduce latency in your app by quickly identifying the most efficient model for a specific task. - Simplify data processing by getting exact counts and segments for your production language workflows. - Create more natural interactions with high-quality conversational responses that handle complex flows with reliability. ## How It Works The bottom line is it turns your agent into a production-ready NLP powerhouse by plugging directly into Cohere's infrastructure. 1. Get your Cohere API Key from your dashboard. 2. Add the key to your agent's configuration. 3. Ask your agent to perform NLP tasks like reranking or embedding generation. ## Frequently Asked Questions **What can I do with the Cohere (AI Platform) MCP?** You can use your agent to generate text, create embeddings for search, and rerank documents to make your search results more accurate. **How does the Cohere (AI Platform) MCP help with RAG?** It improves Retrieval-Augmented Generation by reranking search results to ensure the most relevant information is prioritized for your agent. **Can I use Cohere (AI Platform) MCP to manage my embeddings?** Yes, it allows your agent to create high-dimensional vector representations from text, which is essential for building semantic search systems. **Does the Cohere (AI Platform) MCP support reranking?** Yes, it includes a tool to reorder documents based on semantic relevance, helping you filter out noise in complex search queries. **How do I get my Cohere (AI Platform) MCP connected to my agent?** Just add your Cohere API key to your agent's configuration and you can start using the tools immediately through natural conversation. **Can Cohere (AI Platform) MCP help me save on token costs?** It helps you manage costs by allowing your agent to check model availability and tokenize text to see exactly how many tokens your prompts will use. **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.