How to Use the Cohere (Embed & Rerank) MCP in LangChain

Build complex reasoning chains in LangChain with Cohere (Embed & Rerank) for precise semantic search and document reranking.

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Connect Cohere (Embed & Rerank) MCP to LangChain

Create your Vinkius account to connect Cohere (Embed & Rerank) 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 (Embed & Rerank) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chainable semantic processing in LangChain

Feed your agent outputs directly into `embed_texts` to generate dense vector representations for your retrieval chains. You'll pipe these vectors through your pipeline to maintain state across complex multi-step tasks. Your agents trigger `rerank_documents` to filter search results based on actual relevance scores. This gives your chains the context they need to make decisions without manual sorting.

Inspect model availability for LangChain

Call `list_models` to check which Cohere endpoints your current environment supports before running a heavy workflow. This prevents runtime errors when you switch between model versions in your code. Use `tokenize_text` to calculate exact token counts for your specific input strings. It keeps your LangChain agents within their context windows without guessing.

Controlled classification with LangChain

Map your incoming data to specific categories using `classify_texts` as part of a larger LangChain router. It sorts incoming queries into pre-defined buckets so your logic flows correctly. Execute `chat_completion` to finalize your agent responses using Cohere's specific conversational formatting. This tool handles the heavy lifting of structure while your chain manages the overall flow.

Setup guide

Set up Cohere (Embed & Rerank) 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 (Embed & Rerank) 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-embed-rerank-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 (Embed & Rerank) transactions"
    })
    print(result["messages"][-1].content)

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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 (Embed & Rerank) MCP in LangChain

Install the adapters and initialize the client via HTTP transport. You then register the tools with your agent to start chaining tasks immediately.

Yes, use the reranking tools to sort your retrieved chunks before sending them to the LLM. It drastically improves the signal-to-noise ratio in your RAG pipelines.

It does. You can aggregate the full toolset into your agent's toolkit and call them sequentially or in parallel based on your chain's requirements.

Use LangSmith tracing to monitor every tool call. You'll see latency, token counts, and input payloads for every request sent to the Cohere endpoints.

This MCP Server handles your raw text and document chunks as transient data. We purge these payloads immediately after the API response is returned to your agent.

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