Postman — Business Examples

This guide walks through calling the Datafiniti MCP tools against business data by hand in Postman — selecting a tool, filling in its argument form, sending, and reading the response. It assumes you've already connected Postman to the MCP server and authenticated; if not, start with the Postman guide for download, token, request, and connection setup.

Everything here uses the same tools and data_type parameter as the other data types; what changes are the fields and views specific to business records. For the field reference, see the Business Data Schema and Constructing Business Queries. For the full tool reference, see MCP Server.

The goal

We'll find hotels in the US with locations and contact details — first counting them, then pulling a sample, then filtering by category and a numeric range, then starting a download.

1. Count with df_count

Select the df_count tool and fill in data_type and query:

{
  "data_type": "business",
  "query": "categories:hotels AND country:US"
}

Send it. df_count returns a match count with no records and no credit cost:

{
  "num_found": 58310
}

Screenshot: the``_ argument form and its response._

Count first, spend nothing

A count is free — use it to confirm the query before spending credits. A count of 0 is a valid result, not an error; if you expected matches, check the category and field names against the schema.

2. Search with df_search

Select df_search. Add num_records and a view:

{
  "data_type": "business",
  "query": "categories:hotels AND country:US AND province:NV",
  "num_records": 5,
  "view": [{"name": "name"}, {"name": "address"}, {"name": "city"}, {"name": "province"}, {"name": "postalCode"}, {"name": "phones"}, {"name": "categories"}]
}

Send it. The response has the same shape as a REST search:

{
  "num_found": 1204,
  "total_cost": 5,
  "records": [
    {
      "name": "…",
      "address": "…",
      "city": "Las Vegas",
      "province": "NV",
      "phones": ["…"],
      "categories": ["hotels"]
    }
  ]
}

Screenshot: the``_ argument form and its response._

Quoting exact-match values

Exact-match values use double quotes in the query: name:"Joe's Coffee". Postman sends the JSON body as-is, so escape the inner quotes as \" in the argument form. That's JSON encoding, not query syntax.

3. Search by category and location

Business queries combine field conditions with boolean operators. Restaurants of a cuisine in a city:

{
  "data_type": "business",
  "query": "categories:restaurants AND categories:Italian AND city:"San Francisco""
}

Or match more than one category with parentheses:

{
  "data_type": "business",
  "query": "categories:(hotels OR motels) AND country:US"
}

4. Filter on a numeric range

Numeric fields such as revenue accept range syntax. Businesses within a revenue band:

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

Ranges work the same way for other numeric and date fields on the record.

5. Start a download with df_start_download

For the full set, select df_start_download and add a format:

{
  "data_type": "business",
  "query": "categories:hotels AND country:US",
  "format": "JSON",
  "view": [{"name": "name"}, {"name": "address"}, {"name": "city"}, {"name": "province"}, {"name": "postalCode"}, {"name": "phones"}, {"name": "categories"}]
}

It returns a JSON object with the new download id. Copy the id into df_download_status and poll:

{
  "download_id": "…"
}

The status comes back as queued, running, completed, or cancelled. Once completed, the response includes result-file links, valid for 7 days.

Screenshot: starting a download and polling its status.

Download size follows your plan

How many records a download can return is governed by your subscription plan. To page a smaller set by hand, df_search returns up to 10,000 records across calls.

6. Try a business prompt

Select a prompt such as discover_business_revenue, search_business_by_address, or search_restaurants_by_cuisine from Postman's prompt list. It returns a ready-to-run query; copy that into df_search or df_start_download to run it.

For the full list of business prompts, see Business Data with AI.

Next steps

• Same flow for Property, People, or Product.
• Setup and connection: Postman.
• Full tool reference: MCP Server.

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