How to Use the Showpad MCP in LangChain

Q: How do I ensure the Showpad search results are accurate using LangChain?

The MCP Server supports searchassets by keyword. You can also use listtags first to gather all available tags, which lets your agent filter the keywords for better accuracy before searching.

Q: If I need asset metadata and user details simultaneously with Showpad using LangChain?

You'll run a chain that calls getuserdetails first to identify the relevant stakeholder. Then, you use the retrieved data (like their division) to refine the parameters for calling listassets, ensuring only appropriate content is surfaced.

Q: What kind of Showpad data does LangChain handle regarding privacy?

This MCP Server touches user profiles and asset metadata. The agent can retrieve details on users via getuserdetails or get asset information using getassetdetails.

Q: Can I list all available content channels in Showpad with LangChain?

Yes. You simply call the listchannels tool within your chain. This gives you a comprehensive overview of every channel integrated into the Showpad platform for your AI agent to reference.

Q: How can I use the MCP Server to understand Showpad's organizational structure with LangChain?

You combine listdivisions and listusers. Your agent first gets all divisions, then iterates through them using listusers to map out which people belong to which segment within Showpad.

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Run complex Showpad analyses with LangChain

You can build multi-step decision trees directly on Showpad content. Instead of just searching, your agent first calls `list_divisions` to understand the organizational context, then uses that data to refine a call to `search_assets`. This sequence ensures the final output is highly targeted and actionable. This approach lets you model real-world business logic. For example, if a user needs an asset for 'APAC' (from `list_divisions`), your chain first filters assets using `list_tags` before calling `get_asset_details`. It makes sure the agent follows the right path every time.

Access granular Showpad metadata

The MCP Server gives you deep visibility into what's housed in Showpad. You can use `list_assets` to get a manifest of all available content IDs, and then selectively call `get_asset_details` on any single ID. This saves time because your agent doesn't have to guess which assets are relevant. It also lets you check who owns the content by calling `get_user_details` for specific Showpad users found via `list_users`. You map out ownership and usage patterns, giving a complete picture of the collateral landscape.

Automate content discovery in Showpad with LangChain

Need to find everything related to 'Q3 Compliance'? Your agent can execute `search_assets` with specific keywords and then immediately follow up by calling `list_tags`. This two-step process ensures you capture all assets, even if they are tagged differently than expected. The resulting data is structured for the next step of your reasoning pipeline. This isn't just a search; it’s a sequence. You can combine results from `list_channels` and `search_assets` to build an automated report showing which channels need updated assets, all within one logical chain.

Setup guide

Set up Showpad 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 Showpad 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({
    "showpad-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 Showpad 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 Showpad. 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 Showpad MCP in LangChain

The MCP Server supports `search_assets` by keyword. You can also use `list_tags` first to gather all available tags, which lets your agent filter the keywords for better accuracy before searching.

You'll run a chain that calls `get_user_details` first to identify the relevant stakeholder. Then, you use the retrieved data (like their division) to refine the parameters for calling `list_assets`, ensuring only appropriate content is surfaced.

This MCP Server touches **user profiles** and **asset metadata**. The agent can retrieve details on users via `get_user_details` or get asset information using `get_asset_details`.

Yes. You simply call the `list_channels` tool within your chain. This gives you a comprehensive overview of every channel integrated into the Showpad platform for your AI agent to reference.

You combine `list_divisions` and `list_users`. Your agent first gets all divisions, then iterates through them using `list_users` to map out which people belong to which segment within Showpad.

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