How to Use the Kippy MCP in LangChain

Chain Kippy data directly into your LangChain agents for automated performance tracking and team management.

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Connect Kippy MCP to LangChain

Create your Vinkius account to connect Kippy 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 Kippy with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Direct LangChain tool chaining

Feed `list_kpis` output directly into your next agent step. You define the sequence where one tool result dictates the next query. This MCP Server lets LangChain manage performance data as a series of inputs. You build pipelines where `list_kpi_scores` triggers alerts without manual intervention.

Full LangSmith observability

Watch every `list_audit_logs` call in your trace logs. You see exact latency and token usage for every interaction. Debugging your performance agent becomes trivial when you track inputs and outputs for `list_appraisals` in real time. LangChain records every decision point.

Multi-server context aggregation

Connect Kippy alongside your other databases. LangChain merges `list_teams` data with internal records in a single chain. You create complex reasoning pipelines that pull `list_competencies` and cross-reference them with your vector store results. Everything stays within your defined flow.

Setup guide

Set up Kippy 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 Kippy 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({
    "kippy-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 Kippy 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 Kippy. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Kippy MCP in LangChain

Install the MCP adapters via pip. Use the MultiServerMCPClient to point at your endpoint and pass the tools directly to your agent.

Yes. Since this is an MCP server, LangChain treats every tool as a standard callable function within your LangGraph workflow.

Your data remains local to your execution environment. Vinkius handles the auth, but the actual performance records stay in your private agent memory.

Use the client session object to maintain state. This keeps your user and team lists consistent across multiple turns.

LangChain raises an exception you can catch in your chain. You can then route that error to a recovery agent or log it to your dashboard.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python