How to Use the Cohere (AI Platform) MCP in CrewAI

Q: Can multiple CrewAI agents share the same Cohere (AI Platform) embedding space?

Yes. Agents use the generateembeddings tool to write to a shared memory space, letting different crew members read and compare semantic vectors during execution.

Q: How does Cohere (AI Platform) document reranking improve CrewAI performance?

By running rerankdocuments, your agents filter out noise before passing data to other crew members. This keeps the shared context clean and drastically reduces token overhead.

Equip your CrewAI agent teams with Cohere tools for text generation, document reranking, and input classification.

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Connect Cohere (AI Platform) MCP to CrewAI

Create your Vinkius account to connect Cohere (AI Platform) 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 (AI Platform) with CrewAI

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Collaborative document ranking for agent crews

The `rerank_documents` tool allows your CrewAI research agent to structure contextual chunks before handing them off to an analyst agent. Instead of passing messy, unorganized search results, the researcher uses this tool to prioritize the most relevant text first. By using this MCP Server, you prevent downstream agents from wasting tokens on irrelevant background information. This collaborative pipeline uses shared memory to keep all agents aligned on the current task context.

Specialized input classification and monitoring

The `classify_inputs` tool gives your CrewAI monitor agent the ability to evaluate incoming tasks and delegate them to the correct specialist. The monitor agent runs the classification, then assigns the work based on the returned category. To keep tasks within model limits, a moderator agent can use `tokenize_text` to check the exact segment boundaries. This ensures that no agent attempts to process text that exceeds Cohere's context window.

Multi-agent text generation using this MCP Server

This MCP Server provides the `generate_text` and `chat_generation` tools so multiple CrewAI agents can draft and refine content simultaneously. One agent can generate the initial draft while another uses `generate_embeddings` to verify semantic alignment. You can configure this setup by passing the Vinkius URL directly into the agent's mcps list. If you need strict control over which agents can access which tools, use MCPServerHTTP with a tool_filter to restrict access.

Setup guide

Set up Cohere (AI Platform) 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 (AI Platform) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Cohere (AI Platform) 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 (AI Platform) Analyst",
        goal="Access and analyze Cohere (AI Platform) data via MCP.",
        backstory="Expert analyst with direct Cohere (AI Platform) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Cohere (AI Platform) 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 (AI Platform) MCP in CrewAI

Pass the Vinkius URL in the mcps list of your CrewAI agent. To limit tools, use MCPServerHTTP with a tool_filter to expose only `classify_inputs` or `generate_text` to specific roles.

Yes. Agents use the `generate_embeddings` tool to write to a shared memory space, letting different crew members read and compare semantic vectors during execution.

By running `rerank_documents`, your agents filter out noise before passing data to other crew members. This keeps the shared context clean and drastically reduces token overhead.

The Python SDK supports stdio, SSE, and Streamable HTTP transports. You can choose the transport that matches your deployment environment.

Every request is isolated within an ephemeral V8 sandbox on Vinkius. Your conversational text and semantic vectors are processed in-memory and never written to persistent storage.

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