Cohere MCP Server for AutoGen 6 tools — connect in under 2 minutes
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Cohere as an MCP tool provider through the Vinkius and every agent in the group can access live data and take action.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="cohere_agent",
tools=tools,
system_message=(
"You help users with Cohere. "
"6 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Cohere MCP Server
Connect your Cohere account to any AI agent and leverage enterprise-grade AI models through natural conversation.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Cohere tools. Connect 6 tools through the Vinkius and assign role-based access — a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
What you can do
- Model Discovery — List all available Cohere models with their names, capabilities and context lengths
- Chat API — Send conversations to Command models (command-r-plus, command-r, command-r7b) and receive responses with citations and tool call support
- Embeddings — Generate vector embeddings for semantic search with multiple embedding types (float, int8, uint8, binary)
- Reranking — Rerank documents by relevance to a search query using Cohere's industry-leading reranking models
- Tokenization — Tokenize and detokenize text for estimating token counts and debugging
The Cohere MCP Server exposes 6 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Cohere to AutoGen via MCP
Follow these steps to integrate the Cohere MCP Server with AutoGen.
Install AutoGen
Run pip install "autogen-ext[mcp]"
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Integrate into workflow
Use the agent in your AutoGen multi-agent orchestration
Explore tools
The workbench discovers 6 tools from Cohere automatically
Why Use AutoGen with the Cohere MCP Server
AutoGen provides unique advantages when paired with Cohere through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Cohere tools to solve complex tasks
Role-based architecture lets you assign Cohere tool access to specific agents — a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Cohere tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Cohere tool responses in an isolated environment
Cohere + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Cohere MCP Server delivers measurable value.
Collaborative analysis: one agent queries Cohere while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Cohere, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Cohere data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Cohere responses in a sandboxed execution environment
Cohere MCP Tools for AutoGen (6)
These 6 tools become available when you connect Cohere to AutoGen via MCP:
chat
Requires the model ID (e.g. "command-r-plus", "command-r", "command-r7b") and messages array in JSON format. Each message must have a "role" ("user", "assistant", "system" or "tool") and "content" (text or array of content blocks). Optionally set max_tokens, temperature (0-1), p (nucleus sampling 0-1) and tools array for function calling. Returns the model's response with text, citations and tool calls. Send a chat message to a Cohere model
detokenize
Requires the token IDs array. Returns the reconstructed text. Useful for debugging and verifying tokenization. Detokenize token IDs back to text using Cohere
embed
Requires the model ID (e.g. "embed-v4", "embed-v3"), texts array and input_type ("search_document", "search_query", "classification", "clustering"). Returns embedding vectors for each input text. Useful for semantic search, similarity comparison and vector database storage. Generate embeddings using Cohere
list_models
Each model returns its name (e.g. "command-r-plus", "command-r", "embed-v4", "rerank-v3.5"), endpoint compatibility, context length and tokenization info. Use this to discover which models are available and their capabilities. List all available Cohere models
rerank
Requires the model ID (e.g. "rerank-v3.5", "rerank-english-v3.0"), query text and documents array. Optionally set top_n to return only the top N results. Returns ranked documents with relevance scores. Rerank documents by relevance to a query
tokenize
Requires the text to tokenize and optionally the model. Returns the list of token IDs and token strings. Useful for estimating token counts before sending to chat or embed endpoints. Tokenize text using Cohere
Example Prompts for Cohere in AutoGen
Ready-to-use prompts you can give your AutoGen agent to start working with Cohere immediately.
"Send a message to Command R+ asking 'What is the capital of Brazil?'"
"Rerank these documents for the query 'machine learning models': ['Neural networks are inspired by biological neurons.', 'Python is a popular programming language.', 'Transformers use attention mechanisms for sequence processing.']"
"Generate embeddings for these texts: ['The weather is nice today.', 'I love programming in Python.'] using embed-v4."
Troubleshooting Cohere MCP Server with AutoGen
Common issues when connecting Cohere to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Cohere + AutoGen FAQ
Common questions about integrating Cohere MCP Server with AutoGen.
How does AutoGen connect to MCP servers?
Can different agents have different MCP tool access?
Does AutoGen support human approval for tool calls?
Connect Cohere with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Cohere to AutoGen
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.