How to Use the Data.gov Catalog MCP in LlamaIndex

Q: How do I register the Data.gov Catalog MCP Server with LlamaIndex?

Install llama-index-tools-mcp and initialize the BasicMCPClient with your Vinkius endpoint URL. Wrap it in McpToolSpec and call totoollistasync to pass the tools to your FunctionAgent.

Q: Can I index the output of Data.gov Catalog tools directly into a LlamaIndex vector index?

Yes. You can capture the output of searchdatasets or getharvestrecordtransformed as a Document object, then insert those nodes into your VectorStoreIndex for semantic search.

Q: How do I find the correct organization name for filtering?

Use the getorganizations tool to fetch the complete, official list of publishing agencies. This ensures your agent uses the exact string key required by the catalog search tool.

Index real-time US government metadata into your LlamaIndex vector stores using this MCP Server.

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Connect Data.gov Catalog MCP to LlamaIndex

Create your Vinkius account to connect Data.gov Catalog to LlamaIndex 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 LlamaIndex

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Index MCP Server metadata into vector stores

This MCP Server lets your LlamaIndex RAG pipeline fetch real government metadata on the fly. When your agent calls `search_datasets`, the resulting JSON payload is converted into document nodes and indexed immediately into your local vector database. Don't rely on static, outdated training data; your queries pull fresh records directly from federal sources. This eliminates hallucinations because the agent grounds its answers in the actual documents fetched by the tool.

Map spatial boundaries for geographic RAG

Use `search_locations` to resolve city and state names into specific location IDs. Once you have the ID, `get_location_geometry` retrieves the exact GeoJSON boundary, which your LlamaIndex pipeline uses to filter documents geographically. Combining spatial data with metadata from `get_organizations` lets you build a highly targeted search index. Your agent can query specific regional offices and map their jurisdictions using real coordinates instead of guessing boundaries.

Verify metadata lineage with raw harvest records

When your index contains conflicting dataset entries, your agent runs `get_harvest_record` to check the ingest history. This pulls the exact origin information, allowing the agent to resolve duplicates based on data freshness. For deeper verification, `get_harvest_record_transformed` exposes the clean DCAT-US structure. Your LlamaIndex ingestion pipeline parses these structured fields directly into metadata keys, making your vector search highly filterable by publisher or date.

Setup guide

Set up Data.gov Catalog MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Data.gov Catalog MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Data.gov Catalog tools.",
)
response = await agent.run("List recent Data.gov Catalog data")

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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 LlamaIndex

Install `llama-index-tools-mcp` and initialize the `BasicMCPClient` with your Vinkius endpoint URL. Wrap it in `McpToolSpec` and call `to_tool_list_async` to pass the tools to your `FunctionAgent`.

Yes. You can capture the output of `search_datasets` or `get_harvest_record_transformed` as a Document object, then insert those nodes into your `VectorStoreIndex` for semantic search.

Yes. The tool spec handles asynchronous tool execution natively, allowing your LlamaIndex agent to run multiple `get_location_geometry` queries in parallel without blocking your main execution thread.

Use the `get_organizations` tool to fetch the complete, official list of publishing agencies. This ensures your agent uses the exact string key required by the catalog search tool.

Your catalog search queries and metadata payloads never touch persistent storage on Vinkius. The platform runs the server in an ephemeral, single-tenant container that processes requests in memory and wipes the environment immediately after execution.

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