How to Use the Beeceptor MCP in LangChain

Q: How do I connect Beeceptor to my LangChain agent?

Install langchain-mcp-adapters via pip. Initialize a MultiServerMCPClient with the Vinkius endpoint URL. Call gettools() and pass the array to your ReAct agent constructor.

Q: Does my LangChain pipeline need to hold state between Beeceptor requests?

No, the server operates statelessly by default. Use client.session() if your chain needs persistent context across multiple tool calls.

Q: How do I handle large file uploads for multipart testing?

The server exposes an uploadblob tool for binary data. Your agent can read local files and push them to the mock endpoint before running the target test chain.

Build dynamic mocking chains where LangChain agents use this MCP Server to rewrite API rules on the fly.

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Connect Beeceptor MCP to LangChain

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Setup Beeceptor with LangChain

Ask AI about this MCP

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Claude

Perplexity

Dynamic mock routing with the MCP Server

This Beeceptor integration lets your ReAct agent test edge cases without hitting production. Passing the server to your tool node gives it full control. Now your chain can read incoming traffic with `list_requests` and decide how to respond. The model evaluates the payload against your test requirements. If the test demands a 500 error, the agent calls `create_rule` to force a failure. LangSmith traces the exact latency of every rule change.

Stateful test environments

Stateful mock environments replace messy database fixtures. This setup lets your agent handle data directly through the API layer. Your pipeline runs `upsert_state` to inject test user data before the assertions start. When the suite finishes, cleanup happens automatically. The agent fires off `delete_state` to wipe the slate clean. No leftover junk data polluting your next run.

Automated spec validation

Syncing mock endpoints with OpenAPI definitions usually wastes hours of developer time. Building a chain that watches your repository for changes fixes this. Once a commit lands, the agent pulls the file and triggers `upload_spec`. It then polls `get_job_status` until the parsing finishes. Should the new spec break existing mocks, the agent uses `bulk_replace_rules` to align everything with the updated schema.

Setup guide

Set up Beeceptor 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 Beeceptor 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({
    "beeceptor-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 Beeceptor 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 Beeceptor. 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 Beeceptor MCP in LangChain

Install `langchain-mcp-adapters` via pip. Initialize a `MultiServerMCPClient` with the Vinkius endpoint URL. Call `get_tools()` and pass the array to your ReAct agent constructor.

Yes. Every MCP tool execution registers as a discrete step in LangSmith. You will see exact execution times for calls like setting rules or fetching request history.

No, the server operates statelessly by default. Use `client.session()` if your chain needs persistent context across multiple tool calls.

The server exposes an `upload_blob` tool for binary data. Your agent can read local files and push them to the mock endpoint before running the target test chain.

The Vinkius V8 Isolate Sandbox destroys itself after your session ends. Traffic captured via `list_requests` passes through ephemeral memory. Nothing gets logged or stored permanently outside your immediate execution context.

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