Claude — Business Examples

This guide shows how to get business data out of Datafiniti using Claude. It covers both of Claude's paths: a plain conversation where Claude writes a query and you run it, and connected mode where Claude runs queries itself through the MCP server. For setup of either path, see the Claude page.

For the field reference behind these queries, see the Business Data Schema, Constructing Business Queries, and Possible Values for Business Fields.

The goal

We want US hotels in a specific revenue band — and we'll have Claude build the query, then run it ourselves.

Build the query in conversation

You don't need to connect anything. Paste in the business query guide and the possible-values page, describe what you want, and let Claude draft and explain the query.

You: Using Datafiniti business data, build me a query for hotels in the US with annual revenue between $1M and $5M. Here's the business query guide and possible values: [paste or link the guides]. Explain each part.

Claude: Here's the query:

categories:hotels AND country:US AND revenue:[1000000 TO 5000000]

• categories:hotels matches businesses categorized as hotels — a value from the Possible Values page.
• country:US limits to the United States.
• revenue:[1000000 TO 5000000] uses range syntax to bound annual revenue between 1M and 5M inclusive.

Run the query yourself

Exact-match quoting: " vs \"

When a value is quoted for an exact match (like the one above), which quote form to use depends on where the query goes. In the raw query string, use plain double quotes: FIELD:"value". When that query is a value inside a JSON tool argument or request body, the inner quotes must be escaped as \" so the JSON stays valid: "query": "FIELD:\"value\"". Some coding platforms and clients expect one form over the other — if a query returns an error or no results, check that the quoting matches the context you're pasting into.

Give Claude the right context

Paste in Constructing Business Queries and Possible Values for Business Fields so Claude uses real category values and correct range syntax. Category and field names are the usual place a model guesses wrong.

Take the query Claude produced and run it in Postman, cURL, or an SDK. Mind the exact-match quoting (see the note above) — plain " in the raw query, escaped \" inside JSON:

{
  "data_type": "business",
  "query": "categories:hotels AND country:US AND revenue:[1000000 TO 5000000]",
  "num_records": 5,
  "view": [{"name": "name"}, {"name": "address"}, {"name": "city"}, {"name": "province"}, {"name": "phones"}]
}

In a plain conversation Claude only writes the query — nothing is charged until you run it yourself.

Connected mode: let Claude run it

If you've connected the Datafiniti MCP server to Claude (see the Claude page), ask it to run the query directly:

You: How many US hotels have revenue between $1M and $5M? Show me the first few in Nevada.

Claude calls df_count first (free) to size the result, then df_search with the added location filter:

{
  "data_type": "business",
  "query": "categories:hotels AND country:US AND province:NV AND revenue:[1000000 TO 5000000]"
}

With the connector active, searches and downloads charge credits against the account you authorized; counting is free. See MCP Server for the full tool reference, and Business Data with AI for business fields and prompts.

Use case: search for specific restaurants

A common business-data workflow is finding restaurants by the kind of food they serve — for lead lists, market research, or enrichment. The Search for specific restaurants guide shows a useful wrinkle: two different fields describe what a restaurant offers, and combining them catches more results than either alone. Here's how Claude builds that up.

Say we want vegan restaurants in California.

The categories field is a list of category keywords scraped from a business's descriptions and menu items. Start there:

You: Using Datafiniti business data, build me a query for vegan restaurants in California using the categories field. Here's the business query guide: [paste or link the guide].

Claude: country:US AND province:CA AND categories:(vegan)

The cuisines field describes the cuisines a location offers, typically drawn from online order menus — a different source than categories. The same search against cuisines:

You: Now the same thing but using the cuisines field instead.

Claude: country:US AND province:CA AND cuisines:(vegan)

Two fields, two sources

categories and cuisines are populated from different data sources, so each catches restaurants the other misses. That's exactly why combining them is worth doing.

Combine the fields for full coverage

Because the two fields come from different sources, OR-ing them together is a catch-all that returns any restaurant tagged vegan by either:

You: Combine both so I don't miss any.

Claude: country:US AND province:CA AND (categories:vegan OR cuisines:vegan)

Run it yourself, or have a connected Claude call df_search — then df_start_download for the full set:

{
  "data_type": "business",
  "query": "country:US AND province:CA AND (categories:vegan OR cuisines:vegan)",
  "format": "JSON",
  "view": [{"name": "name"}, {"name": "address"}, {"name": "city"}, {"name": "province"}, {"name": "categories"}, {"name": "cuisines"}]
}

For the full walkthrough, see Search for specific restaurants.

Next steps

• Same flow for Property, People, or Product data.
• Setup for both paths: Claude.
• Full tool reference: MCP Server.

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