Cohere (AI Platform) Connector for AI agents.
5 live capabilities
Build production-ready RAG systems and semantic search capabilities.
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Why people use Cohere (AI Platform)
Cohere (AI Platform) for RAG and Semantic Search
With this Connector, your agent handles the heavy lifting. It takes your raw search results and uses rerank_documents to put the best answers at the top instantly. You get a clean, prioritized list of information without the manual filtering.
What Vinkius changes
It turns your agent into a production-ready NLP powerhouse by plugging directly into Cohere's infrastructure.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Evaluating chunking strategies
A data scientist needs to see if a new chunking strategy improves search.
- Real-world use case 02
Calculating token limits
A product manager wants to see how many tokens a 50-page PDF uses.
- Real-world use case 03
Building a product search
A dev needs to build a product search.
Complete set · 5capabilities
The complete Cohere (AI Platform) capability set.
These are the exact actions your AI can choose when you ask it to work with Cohere (AI Platform).
01—03
3 capabilities in this set.
Part of 5 available through Cohere (AI Platform).
- 01 Capability
Generate embeddings
Create dense vector representations from your text. This allows your agent to build and query a semantic search index.
- 02 Capability
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.
- 03 Capability
Chat completion
Get human-like responses from Cohere's chat models. Use this for building conversational interfaces or summarizing long text.
04—05
2 capabilities in this set.
Part of 5 available through Cohere (AI Platform).
- 04 Capability
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.
- 05 Capability
List models
See every model available on your specific Cohere plan. Use this to check if you have access to the latest releases.
Set up in minutes
One URL. Then ask Cohere (AI Platform) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Cohere (AI Platform) from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_fA1l2oqJxpD7QL48YxxEDHfZLGLU7FMkmFVJY7Ft/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Cohere (AI Platform), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Cohere (AI Platform) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_fA1l2oqJxpD7QL48YxxEDHfZLGLU7FMkmFVJY7Ft/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Cohere (AI Platform) URL.
- Step 03
Save and start
Save the connection and enable Cohere (AI Platform) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"cohere-ai-platform": {
"url": "https://edge.vinkius.com/vk_preview_fA1l2oqJxpD7QL48YxxEDHfZLGLU7FMkmFVJY7Ft/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Cohere (AI Platform)
Open Agent mode in chat and ask: "Using Cohere (AI Platform), help me...". 5 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"cohere-ai-platform": {
"url": "https://edge.vinkius.com/vk_preview_fA1l2oqJxpD7QL48YxxEDHfZLGLU7FMkmFVJY7Ft/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Cohere (AI Platform)
Ask Copilot: "Using Cohere (AI Platform), help me...". 5 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"cohere-ai-platform": {
"url": "https://edge.vinkius.com/vk_preview_fA1l2oqJxpD7QL48YxxEDHfZLGLU7FMkmFVJY7Ft/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Cohere (AI Platform)
Open Cascade and ask: "Using Cohere (AI Platform), help me...". 5 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"cohere-ai-platform": {
"url": "https://edge.vinkius.com/vk_preview_fA1l2oqJxpD7QL48YxxEDHfZLGLU7FMkmFVJY7Ft/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Cohere (AI Platform)
Ask Cline: "Using Cohere (AI Platform), help me...". 5 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add cohere-ai-platform --transport http "https://edge.vinkius.com/vk_preview_fA1l2oqJxpD7QL48YxxEDHfZLGLU7FMkmFVJY7Ft/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Cohere (AI Platform)
Ask Claude: "Using Cohere (AI Platform), show me...". 5 tools are ready
Where the request belongs
Work Cohere can move forward.
This is for the engineers and data scientists who need to move beyond basic chat and build high-performance search, reranking, and embedding pipelines without the headache of manual infrastructure management.
AI Developer
Testing chat completion logic and debugging tokenization limits without switching tabs.
Data Scientist
Evaluating reranking scores and embedding quality for RAG pipelines in real-time.
Product Manager
Quickly prototyping new generative features using enterprise-grade models.
NLP Engineer
Auditing model behavior and checking token counts for large-scale data processing.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsCohere
Access Cohere AI models via API. chat with Command models, generate embeddings, rerank documents and tokenize text from any AI agent.
Voyage AI (AI Embeddings API)
Generate high-quality text, multimodal, and contextualized embeddings, plus high-precision reranking for RAG workflows.
Gradient AI (LLM API & Finetuning)
Access powerful LLMs, fine-tune models on your own data, and generate embeddings directly through your AI agent.
Cohere (Embed & Rerank)
Empower RAG via Cohere. generate high-quality text embeddings, rerank documents for better accuracy, and perform AI classification directly from any AI agent.
Mistral AI (Frontier LLMs & Embeddings)
Manage AI inference via Mistral. execute chat completions, generate RAG embeddings, and audit frontier models.
Eden AI
Access 100+ AI models through a single API. route LLMs, generate embeddings, and execute specialized AI tasks like OCR and translation.
Bring your own AI
Change the model, client or framework. Keep Cohere connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Cohere.
The practical details behind the request, access and result.
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 capability 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 capabilities 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' capabilities 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' capability. 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' capability 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.
One connection away
Give your agent a direct line to Cohere.
Connect Cohere once. Keep it beside 5,900+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available