How to Use the ConfigCat MCP in LangChain

Q: How do I configure ConfigCat tools inside a LangChain agent?

Use the MultiServerMCPClient to load the tools and pass them to createagent. This exposes tools like listconfigs to your agent's toolbelt.

Q: How does LangChain track the latency of ConfigCat tool calls?

LangSmith automatically traces every call to tools like getsetting. You get a visual timeline of how long the ConfigCat API takes to respond inside your chain.

Q: Can I use this MCP Server with multiple environments?

Yes, the server exposes listenvironments and createenvironment so your agent can navigate and manage flags across your entire pipeline.

Control feature flags inside your LangChain reasoning loops.

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Create your Vinkius account to connect ConfigCat 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 ConfigCat with LangChain

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Claude

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Chain ConfigCat flag checks with app logic

LangChain agents can check flag states using this MCP Server before executing downstream steps. When your agent runs a chain, it calls `get_setting_value` to decide whether to fetch data from a legacy database or a new API. This stops the agent from executing broken code paths in production. The output of this check flows directly into the next chain link. If a feature is off, the agent pivots and uses alternative tools, keeping the entire reasoning loop intact without manual intervention.

Track flag changes with LangSmith tracing

Every time your LangChain agent calls `update_setting_value` to toggle a feature, LangSmith logs the exact payload. You see the latency of the tool call and the exact targeting rules modified during the run. This visibility makes debugging agent mistakes simple. If your agent incorrectly uses `create_segment` during a test run, you can trace the inputs directly back to the prompt that triggered the action.

Build multi-step LangChain MCP Server release loops

This MCP Server lets you build complex deployment pipelines where the agent decides when to release code. The agent can run `list_environments` to find your staging target, check its status, and then run `create_setting` to spin up a new flag. Because LangChain handles multi-step loops naturally, your agent can verify staging metrics before calling `update_setting_value` to roll the change out to production.

Setup guide

Set up ConfigCat 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 ConfigCat 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({
    "configcat-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 ConfigCat transactions"
    })
    print(result["messages"][-1].content)

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Common questions about ConfigCat MCP in LangChain

Use the `MultiServerMCPClient` to load the tools and pass them to `create_agent`. This exposes tools like `list_configs` to your agent's toolbelt.

Yes, you can build a ReAct agent that catches exceptions in your code and calls `update_setting_value` to turn off a buggy feature. This acts as an automated circuit breaker.

LangSmith automatically traces every call to tools like `get_setting`. You get a visual timeline of how long the ConfigCat API takes to respond inside your chain.

Yes, the server exposes `list_environments` and `create_environment` so your agent can navigate and manage flags across your entire pipeline.

Only the flag metadata and targeting rules required by tools like `get_setting_value` are sent to your LLM provider. The Vinkius MCP sandbox keeps your raw API keys isolated and secure.

Use it with your favorite AI tools

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