How to Use the Ebby MCP in LangChain

Q: How do I pass Ebby tools to a LangChain agent?

Install langchain-mcp-adapters and use MultiServerMCPClient to connect the Ebby server to your LangChain agent. Call client.gettools() to retrieve the Ebby tools, then pass that list directly to your LangChain createagent call.

Q: Can my LangChain agent monitor active Ebby jobs?

Yes, your LangChain agent can poll listinprogresstranscriptions to see what Ebby files are processing. Once the status changes, the LangChain agent calls listsuccessfullyprocessedaudio to pull the finished transcripts.

Feed Ebby transcription data directly into your LangChain reasoning loops using this MCP Server.

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Connect Ebby MCP to LangChain

Create your Vinkius account to connect Ebby 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 Ebby with LangChain

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Claude

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Automate multi-step audio analysis in LangChain

Run your LangChain agent to monitor the Ebby transcription lifecycle without writing manual polling scripts. The LangChain agent triggers `list_in_progress_transcriptions` to watch active audio files, then pipes that status directly into the next chain link. If an audio job takes too long, your LangChain agent uses `get_transcription_details` to check the Ebby status and decides whether to wait or pivot. LangSmith tracks this entire chain, showing you exactly how much context the transcription text consumes.

Targeted speaker retrieval in LangChain chains

Stop feeding massive, raw audio logs to your LangChain chains and blowing your LLM token budget. Use this MCP Server to let your LangChain agent call `list_transcription_speakers` to isolate specific Ebby speakers before running further chain operations. By combining `search_transcriptions_by_name` with speaker metadata inside LangChain, your agent builds a clean map of the Ebby conversation. It passes these filtered blocks to your LangChain prompt templates, avoiding long, irrelevant transcripts.

Audit Ebby account limits within active LangChain chains

Your LangChain agent can call `get_ebby_account_metadata` at the start of a chain to verify remaining Ebby limits before initiating heavy audio tasks. If Ebby limits are tight, the LangChain agent pivots to processing smaller files. You can also use `quick_transcription_volume_audit` to let the LangChain agent assess overall Ebby system load. This keeps your LangChain pipelines self-aware, preventing failed Ebby API calls.

Setup guide

Set up Ebby 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 Ebby 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({
    "ebby-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 Ebby 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 Ebby. 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 Ebby MCP in LangChain

Install `langchain-mcp-adapters` and use `MultiServerMCPClient` to connect the Ebby server to your LangChain agent. Call `client.get_tools()` to retrieve the Ebby tools, then pass that list directly to your LangChain `create_agent` call.

Yes, your LangChain agent can poll `list_in_progress_transcriptions` to see what Ebby files are processing. Once the status changes, the LangChain agent calls `list_successfully_processed_audio` to pull the finished transcripts.

You should use a LangChain text splitter right after calling the Ebby `get_transcription_text` tool. This keeps the retrieved Ebby transcript within your LangChain model's context window limits.

Yes, every time your LangChain agent invokes Ebby tools like `search_transcriptions_by_name`, LangSmith logs the input, output, and execution latency of the MCP Server call.

Your Ebby audio transcripts and speaker metadata stay secure inside the Vinkius sandboxed environment. The `langchain-mcp-adapters` client only receives the text strings returned by `get_transcription_text` over an encrypted endpoint.

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