How to Use the Mode Analytics MCP in LangChain

Q: Can LangChain debug Mode Analytics report runs?

Yes. You can use getmodereportrun to check status and logs. This lets your agent handle failures or retries automatically within your LangChain trace.

Chain Mode Analytics tools directly into your LangChain agents to automate complex data workflows without leaving your pipeline.

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Connect Mode Analytics MCP to LangChain

Create your Vinkius account to connect Mode Analytics 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 Mode Analytics with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate report execution in chains

Trigger `run_mode_report` within your LangChain pipeline to fetch fresh data automatically. Your agent handles the sequence, ensuring the report finishes before passing results to the next step. Stop waiting on manual refreshes. By connecting this MCP Server, your agent manages the entire loop from triggering the run to parsing the final output for your application.

Discover definitions and queries

Use `list_mode_queries` and `list_mode_definitions` to let your agent inspect SQL logic directly. It understands what's happening under the hood before executing any heavy data tasks. This visibility prevents errors. Your agent checks the existing logic first, ensuring it works with the correct calculated fields rather than guessing at the structure.

Map spaces and team members

Fetch workspace metadata using `list_mode_spaces` or check permissions with `list_mode_members`. It keeps your agent informed about where data lives and who has access. You avoid unauthorized calls by verifying space IDs first. Your agent uses these tools to navigate the organization, making sure every request hits the right destination.

Setup guide

Set up Mode Analytics 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 Mode Analytics 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({
    "mode-analytics-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 Mode Analytics 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 Mode Analytics. 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 Mode Analytics MCP in LangChain

Install the necessary adapters and initialize the MCP client pointing to your server URL. Once connected, fetch the tool list and inject them into your agent's execution environment.

Yes. You can use `get_mode_report_run` to check status and logs. This lets your agent handle failures or retries automatically within your LangChain trace.

It does. You can aggregate multiple MCP sources into one pipeline. Your agent will decide which server to query based on the task requirements.

The server acts as a bridge. It converts your agent's natural language requests into specific API calls, ensuring only the requested data returns to your workflow.

The tool returns the error details from the API. You can write your agent logic to catch these responses and report them back to you or attempt a different action.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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