How to Use the Cincopa MCP in LangChain

Q: How do I initialize the Cincopa MCP Server in LangChain?

First, install langchain-mcp-adapters and langgraph in your environment. Next, initialize the MultiServerMCPClient pointing to your Vinkius endpoint. Finally, pull the tools with client.gettools() and pass them to your agent constructor.

Q: Can a LangChain agent manage Cincopa templates and galleries autonomously?

Yes, it can. The agent uses listcincopatemplates to select a design and createnewgallery to build it. It evaluates the output of each tool to decide the next step dynamically.

Q: How does LangSmith help debug Cincopa MCP Server tool failures?

LangSmith logs every single call to tools like getmediauploadurl or deletemediaasset. You can inspect the exact payloads, response times, and LLM decisions if an upload or deletion fails.

Connect Cincopa to your LangChain agents to automate video gallery curation and track media assets in multi-step chains.

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Connect Cincopa MCP to LangChain

Create your Vinkius account to connect Cincopa 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 Cincopa with LangChain

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Build Cincopa media pipelines in LangChain

Your LangChain agent can now run multi-step media workflows without hardcoded API calls. By combining `list_cincopa_templates` and `create_new_gallery`, the agent reviews available layouts and spins up new galleries on the fly based on user requests. Every tool execution is fully observable in LangSmith. You can track latency and token usage as the agent fetches a temporary URL via `get_media_upload_url` to prepare an asset upload, ensuring your automated video pipelines run reliably.

Trace Cincopa asset metadata with LangSmith

Debugging media workflows is painful when you don't know why an asset failed to load. This MCP Server lets your ReAct agents call `get_asset_metadata` to inspect video formats, aspect ratios, and hosting details, passing that raw data directly to the next chain link. Because LangChain manages these transitions as discrete steps, you see exactly what metadata was retrieved. If an agent decides to purge an old file using `delete_media_asset`, you can trace the decision path in your logs to verify the logic.

Sync Cincopa galleries with LangChain databases

Combine Cincopa media management with over 500 LangChain integrations to sync your external databases. The agent can query existing setups with `list_cincopa_galleries` and update your internal CMS records in a single execution loop. Using `get_gallery_details`, your agent extracts specific embed codes and structural settings. It then feeds this structured output directly into your database connectors, keeping your external video portals and internal systems aligned.

Setup guide

Set up Cincopa 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 Cincopa 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({
    "cincopa-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 Cincopa 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 Cincopa. 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 Cincopa MCP in LangChain

First, install `langchain-mcp-adapters` and `langgraph` in your environment. Next, initialize the `MultiServerMCPClient` pointing to your Vinkius endpoint. Finally, pull the tools with `client.get_tools()` and pass them to your agent constructor.

Yes, it can. The agent uses `list_cincopa_templates` to select a design and `create_new_gallery` to build it. It evaluates the output of each tool to decide the next step dynamically.

LangSmith logs every single call to tools like `get_media_upload_url` or `delete_media_asset`. You can inspect the exact payloads, response times, and LLM decisions if an upload or deletion fails.

By default, the MCP client is stateless. If you need your agent to remember previous metadata from `get_asset_metadata` across steps, use `client.session()` to maintain active context.

Vinkius runs the server inside a zero-trust V8 Isolate Sandbox. Your media assets, gallery metadata, and API keys are processed in ephemeral environments, meaning no sensitive Cincopa credentials or video files are permanently stored on our servers.

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