How to Use the Cohere MCP in CrewAI

Q: Can a CrewAI agent use Cohere to rank documents?

Yes, you can assign the rerank tool to a specific researcher agent. This agent will use Cohere's ranking models to filter raw documents before sharing them with the rest of the crew.

Q: Which Cohere models can my CrewAI agents use?

Your agents can run the listmodels tool to see all available endpoints. They can then dynamically target models like Command-R-Plus for complex reasoning or Command-R for quick tasks.

Equip your CrewAI agent teams with Cohere's powerful reranking, embedding, and chat tools for autonomous operations.

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Connect Cohere MCP to CrewAI

Create your Vinkius account to connect Cohere to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cohere with CrewAI

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Equip CrewAI agent teams with Cohere MCP Server tools

The Cohere `rerank` tool allows your CrewAI research agents to filter raw web search results before passing them to the writer agents. Instead of dumping messy data into CrewAI's shared memory, the researcher agent uses Cohere so you aren't wasting context space. This CrewAI multi-agent setup prevents information overload across your crew. By filtering out noise early using Cohere, your specialized CrewAI writer agents can produce cleaner drafts without getting bogged down in irrelevant details.

Coordinate crew discussions using Command-R

The Cohere `chat` tool serves as the primary engine for your individual CrewAI agents to communicate and debate tasks. Whether they are analyzing market trends or drafting blog posts, they use Cohere's Command-R models to generate structured updates. Because this Cohere tool returns citations, your CrewAI monitoring agents can verify the sources used by the execution agents. This creates a transparent audit trail for every decision your CrewAI crew makes autonomously.

Track token usage across complex crew runs

The Cohere `tokenize` tool gives your CrewAI moderator agents a way to measure the exact size of the shared context before kicking off a new task. This prevents your CrewAI crew from hitting Cohere context window limits during long, multi-turn collaborations. You can set up a CrewAI budget agent that monitors Cohere token counts dynamically. If a CrewAI task's context gets too bloated, the agent can trigger a summarization step using Cohere to keep the run cost-effective.

Setup guide

Set up Cohere MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Cohere tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Cohere Analyst",
    goal="Access and analyze Cohere data via MCP.",
    backstory="Expert analyst with direct Cohere access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Cohere transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Cohere Analyst",
        goal="Access and analyze Cohere data via MCP.",
        backstory="Expert analyst with direct Cohere access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Cohere transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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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Common questions about Cohere MCP in CrewAI

Pass the Vinkius endpoint URL directly into your CrewAI Agent configuration using the `mcps` parameter. The framework automatically discovers and registers all six tools for your agents.

Yes, you can assign the `rerank` tool to a specific researcher agent. This agent will use Cohere's ranking models to filter raw documents before sharing them with the rest of the crew.

CrewAI handles tool execution errors gracefully by retrying failed calls. If the `chat` tool hits a rate limit, the executing agent will wait and retry before reporting back to the manager agent.

Your agents can run the `list_models` tool to see all available endpoints. They can then dynamically target models like Command-R-Plus for complex reasoning or Command-R for quick tasks.

Your search queries, text documents, and token payloads are transmitted securely via encrypted TLS directly to the Cohere API. Vinkius runs this MCP Server in an isolated, ephemeral sandbox that destroys all session data immediately after execution.

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