How to Use the Castmagic MCP in LangChain

Turn audio into searchable chain links with LangChain and the Castmagic MCP Server.

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Connect Castmagic MCP to LangChain

Create your Vinkius account to connect Castmagic 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 Castmagic with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate audio pipelines in LangChain

Feed media URLs directly into your agentic loops using `create_new_transcript`. The tool output flows immediately into downstream nodes, letting your chain handle the heavy lifting without manual triggers. Your agents can monitor progress by calling `list_transcripts` to verify when files are ready for processing. It keeps your logic moving forward.

Extract content using LangChain chains

Trigger `get_magic_content` to pull show notes or social copy into your existing LangGraph state. You decide if the output needs a summary or a rewrite before it hits your database. Every tool call is tracked in LangSmith. You see exactly how the `get_magic_content` result influences the next step in your reasoning chain.

Manage media records via LangChain

Clean up your workspace by using `delete_transcript` when a project completes. It clears out old data so your agent stays focused on the active queue. Check your usage with `get_castmagic_account` inside a monitoring node. You get precise feedback on how many transcriptions remain in your monthly quota.

Setup guide

Set up Castmagic 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 Castmagic 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({
    "castmagic-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 Castmagic 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 Castmagic. 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 Castmagic MCP in LangChain

Install the MCP adapters and point your LangChain agent to the Vinkius endpoint URL. Pass the tools retrieved from the server into your agent constructor to enable direct access.

Yes, you can iterate through a list of URLs using a loop inside your chain. Each file is submitted via `create_new_transcript` as a separate task.

We keep your audio and transcript data isolated within your account. The connection uses your private token so only your specific LangChain instance accesses your information.

The server handles requests asynchronously, allowing your agent to continue other tasks while waiting for transcription to finish. You can poll the status using the list tools.

Wrap your tool execution in a standard try-except block within your chain. If a call fails, your agent can log the error or retry based on your custom logic.

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