How to Use the Cohere MCP in LangChain

Q: How do I rerank search results using Cohere and LangChain?

You pass your document list and query into the rerank tool. The server returns the sorted list with relevance scores, which you then pass to the next node in your LangChain sequence.

Q: Will Cohere tokenization help my LangChain costs?

Absolutely. You use the tokenize tool to count your inputs before sending them to the chat endpoint. This keeps your LangChain app within budget by avoiding unnecessary token usage.

Build complex reasoning chains with LangChain by hooking directly into Cohere's Command and Rerank models.

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Connect Cohere MCP to LangChain

Create your Vinkius account to connect Cohere to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Cohere models inside LangChain workflows

Feed the output of one step directly into the next by using the `chat` tool within your LangGraph pipelines. You define the sequence and let the agent handle the flow between nodes. This setup keeps your logic clean. The agent decides when to trigger `chat` or `rerank` based on the data flowing through your chain.

Trace Cohere tool calls in LangSmith

Monitor exactly what your agent is doing when it calls `list_models` or `tokenize`. Every input and output gets logged so you can see how the model behaves at each step. Debugging becomes simple when you can inspect the raw data. You will catch errors in your prompt logic before they hit your production pipeline.

Manage context with LangChain sessions

Use the client session feature to keep track of multi-turn conversations with Command-R. This prevents your agent from losing the thread during long-running tasks. Storing state this way ensures your chain remains consistent. You get reliable interactions without manually passing history arrays back and forth.

Setup guide

Set up Cohere MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Cohere tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "cohere-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Cohere transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Cohere MCP in LangChain

Yes, you connect this MCP server to your LangChain agent and it gains immediate access to all six tools. The agent treats them as native functions for reasoning and data processing.

You pass your document list and query into the `rerank` tool. The server returns the sorted list with relevance scores, which you then pass to the next node in your LangChain sequence.

Absolutely. You use the `tokenize` tool to count your inputs before sending them to the chat endpoint. This keeps your LangChain app within budget by avoiding unnecessary token usage.

It does. You can aggregate this server with others to give your agent a broader set of tools to work with.

Your message content stays between your local agent and the API. Vinkius operates a zero-trust sandbox where no logs of your chat data are stored or inspected.

Use it with your favorite AI tools

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