Mindcase

A closer look at Mindcase

Mindcase provides web data APIs for AI agents: 84 APIs across 25-plus sources, including LinkedIn, Google Maps and Amazon, returning clean, structured data such as employees with title, location and profile URL, places with ratings and reviews, and products with price and rating counts, with keyless runs and an MCP console.

Editorial guidance based on published documentation. The workflow below is a suggested starting point, not a measured product result.

When to choose Mindcase

• AI agent builders who need clean, structured web data from many sources without building a scraper per site.
• Teams that need LinkedIn, Google Maps or Amazon data in a uniform API shape for an agent workflow.
• Builders who want to test an API run before buying credits, since the console offers keyless runs that execute server-side.

When to choose something else

• Buyers who expect subscription plans or seat pricing, since Mindcase states there are no plans, no seats and no complicated pricing: credits are bought as usage bundles.
• Users who need a source outside the 84 listed APIs, since coverage is limited to the listed APIs, with custom sources offered as a build-and-run service.
• Teams that expect credits to expire on a schedule, since the pricing page states credits never expire.

A practical starting workflow

1. Open the console or copy a prompt, or connect through MCP.
2. Start a new run, pick an API such as LinkedIn Company Employees, and set the maximum results.
3. Run it keyless or with a key, then use the structured records in the agent.

Check before committing

• Per-record prices are shown in cents in the console, such as 0.4 cents per employee, so a large run's total depends on record count and API.
• Credit bundles carry a bonus that grows with size, so the amount paid and the usage received differ; the pricing page states both figures.

Common questions

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