How to Use the Data.gov Catalog MCP in LangChain

Q: How do I connect the Data.gov Catalog MCP Server to a LangChain agent?

Install langchain-mcp-adapters and use the MultiServerMCPClient to point to the Vinkius endpoint URL. Call client.gettools() to extract the tools and pass them directly to your createagent function.

Q: What is the difference between raw and transformed harvest records?

Raw records from getharvestrecordraw show the exact payload ingested from the source agency. Transformed records from getharvestrecordtransformed use the DCAT-US schema, which makes it easier for your agent to parse fields reliably.

Feed clean US government metadata directly into your LangChain pipelines using this MCP Server.

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Connect Data.gov Catalog MCP to LangChain

Create your Vinkius account to connect Data.gov Catalog 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 Data.gov Catalog with LangChain

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Trace Data.gov Catalog queries with LangChain

This MCP Server exposes federal metadata tools directly to your LangChain agents. When you call `search_datasets`, the raw JSON payload feeds into your active chain, letting the agent decide which agency to query next based on real-time search results. You track every single API payload transition inside LangSmith. If `get_harvest_record` takes too long or returns unexpected schemas, you'll see the exact latency and token usage in your tracing dashboard without writing custom logging wrapper code.

Build multi-step federal data pipelines

Using this MCP integration, your agent uses `get_organizations` to map out active government publishers, then filters those groups programmatically. By chaining this output with `get_keywords`, the agent pinpoints specific agency focuses before pulling any raw metadata. The model handles the decision tree on its own. Instead of hardcoding API endpoints, you let the agent inspect `get_location_geometry` to resolve spatial boundaries before passing those coordinates to the next chain link.

Filter and transform raw federal harvest records

Raw government data is messy, but `get_harvest_record_transformed` cleans up the output into a standardized DCAT-US format. Your LangChain agent ingests this sanitized structure, ensuring downstream parsers don't break on unexpected fields. If you need the unedited source file, it's easy: `get_harvest_record_raw` pulls the exact payload as ingested by Data.gov. This lets your agent compare original and transformed schemas to catch mapping errors before they hit your production database.

Setup guide

Set up Data.gov Catalog 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 Data.gov Catalog 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({
    "datagov-catalog-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 Data.gov Catalog 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 Data.gov. 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 Data.gov Catalog MCP in LangChain

Install `langchain-mcp-adapters` and use the `MultiServerMCPClient` to point to the Vinkius endpoint URL. Call `client.get_tools()` to extract the tools and pass them directly to your `create_agent` function.

Yes. The client manages multiple endpoints simultaneously, allowing your agent to query federal datasets via `search_datasets` and combine that info with tools from other services in a single session.

You configure standard LangChain runnable configurations to handle retries and backoffs. Because Vinkius hosts the MCP Server, the connection layer remains stable even when running heavy parallel queries across `get_keywords` or `get_organizations`.

Raw records from `get_harvest_record_raw` show the exact payload ingested from the source agency. Transformed records from `get_harvest_record_transformed` use the DCAT-US schema, which makes it easier for your agent to parse fields reliably.

Vinkius runs this tool in an isolated V8 sandbox, meaning your catalog search queries and harvest records are never stored or used for training. All transport encryption terminates at your private endpoint, protecting your operational metadata.

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