How to Use the Apidog MCP in LangChain

Build multi-step chains in LangChain that pull live API definitions directly from your Apidog workspace.

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Connect Apidog MCP to LangChain

Create your Vinkius account to connect Apidog 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 Apidog with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain API data into your LangChain agents

Feed your agent project lists and schema details using `list_projects` and `list_schemas`. The model chains these outputs to plan its next move without manual intervention. Your LangChain pipeline treats every tool response as a link. It processes the raw JSON from `export_openapi` to generate code or documentation based on current project state.

Trace every Apidog call in LangSmith

Every action taken by the MCP server shows up in your LangSmith traces. You see exactly what the agent received from `get_endpoint` during a complex reasoning step. Debugging becomes trivial when you inspect the latency and token usage of specific tool calls. You know why an agent chose a specific endpoint because the history is right there.

Aggregate multiple servers in your logic

Combine the Apidog MCP Server with your existing SQL or vector store tools inside one LangGraph workflow. Your agent navigates between project metadata and database records seamlessly. Stateless execution ensures each chain run stays isolated. Use the session handler if you need persistent context across long-running development tasks.

Setup guide

Set up Apidog 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 Apidog 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({
    "apidog-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 Apidog 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 Apidog. 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

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Common questions about Apidog MCP in LangChain

Install the required adapters and initialize the MultiServerMCPClient with your endpoint. Pass the tool list into your agent constructor to enable immediate access to your workspace data.

Yes, you can share these tools across multiple agents in a single graph. Each agent requests the specific endpoint definitions it needs to complete its assigned task.

The agent uses the schema information returned by the server to decide which endpoints to call. It reads the exported specs to understand the available parameters before making a request.

We only handle your project definitions and OpenAPI specifications. Your data never leaves the Vinkius sandbox, and you control access via a single endpoint token.

Monitor the tool inputs and outputs directly through your standard tracing interface. Every JSON blob returned by the server is logged for your review.

Use it with your favorite AI tools

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