How to Use the Bitly MCP in LangChain

Shorten links and track performance directly within LangChain chains for data-driven agent workflows.

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Connect Bitly MCP to LangChain

Create your Vinkius account to connect Bitly 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 Bitly with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Link Shortening in LangChain

Feed your agent a long URL and let it trigger `shorten_url` to get a branded link back. The output flows directly into your next chain step without manual copying. Your agent handles the parameters for custom domains and titles instantly. It keeps the context moving through your LangSmith traces.

Automated Click Reporting

Run `get_clicks` or `get_referrers` to pull live data into your agent's reasoning loop. You can turn raw numbers into immediate decisions about where to push traffic. This MCP Server provides the raw numbers for your agent to analyze. It identifies top performers without you leaving the dev environment.

Manage Bitlinks Programmatically

Use `list_bitlinks` to audit your existing assets while building complex logic. Your agent sorts through groups to find the exact link needing an update. Call `update_bitlink` to change titles based on the results of your analysis. It keeps your link organization synced with your agent's latest findings.

Setup guide

Set up Bitly 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 Bitly 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({
    "bitly-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 Bitly 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 Bitly. 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 Bitly MCP in LangChain

You install the MCP adapters and pass the server URL to the client. This allows your LangChain agent to invoke Bitly functions as native tools.

Yes, it can. The agent pulls click data into the chain and uses that information to decide which links to promote next.

It does. You can aggregate this server with your databases and vector stores to create a unified toolset for your agent.

The data stays within your agent's memory and LangSmith traces. You control the flow from the tool call to the final output.

Vinkius handles your authentication via a single endpoint token. Your agent only accesses the specific click counts and link metadata you authorize.

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

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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