How AI Agents Buy Data via MCP in 30 Seconds

Agentic workflows hit data gaps constantly: a backtest needs fundamentals, a RAG pipeline needs a domain corpus, an evaluation needs labeled examples. The usual answer is a human detour — someone googles, signs up for an API, wrangles a CSV. DataBazaar removes the detour: it's a data marketplace your agent can search and buy from directly, over the Model Context Protocol.

Here's the whole loop, from zero to downloaded dataset.

Step 1 — Install the MCP server (one command)

For Claude Code:

claude mcp add databazaar -- npx databazaar-mcp

For Claude Desktop, Cursor, or any other MCP client, add the server to your MCP config:

{
  "mcpServers": {
    "databazaar": {
      "command": "npx",
      "args": ["databazaar-mcp"]
    }
  }
}

No API key is needed to search, preview, or download free datasets. A key (for purchases and selling) comes later — and your agent can request one itself.

Step 2 — Search and evaluate

Your agent now has marketplace tools. It searches by topic, inspects the schema, and pulls a free sample before committing to anything:

search_datasets({ query: "us equity fundamentals quarterly" })
// → ranked listings with price, format, row count, schema summary

get_dataset({ dataset_id: "..." })
// → full schema, license, seller, reviews

preview_sample({ dataset_id: "..." })
// → first rows of the actual data, before paying

The sample is real data from the listing, so the agent can verify columns, coverage, and quality match the task — the same diligence a human would do, minus the human.

Step 3 — Buy (or just download, if it's free)

Free datasets skip payment entirely:

get_download_url({ dataset_id: "..." })
// → signed URL, valid for a limited window

Paid datasets settle through Stripe with escrow — funds release to the seller 24 hours after a successful download, so there's recourse if the data misrepresents itself:

buy_now({ dataset_id: "..." })
// → purchase record + signed download URL
// → receipt available via get_purchase_receipt

When the data doesn't exist yet: bounties

The most interesting failure mode is a search that comes back empty. Instead of giving up, the agent logs the gap — which can post a public bounty that human data sellers compete to fulfill:

log_data_gap({
  query: "EV charging station uptime by network, 2024-2026",
  suggested_price_usd: 150
})
// → gap recorded; high-value gaps surface as public bounties

Unmet agent demand becomes a priced signal for what data is worth creating. Browse what agents are asking for at databazaar.io/bounties.

Prefer raw HTTP? The REST API works too

Everything the MCP server does is also a REST call — GET https://api.databazaar.io/datasets?query=... to search, POST /datasets/{id}/buy to purchase. The full agent-readable spec lives at api.databazaar.io/llms.txt and the OpenAPI document at /openapi.json.