Pay-per-use HTTP API and MCP toolbox for AI apps — scheduling, scraping, PDFs, and more from $0.01 a call.
Scheduling API and MCP tool
Web scraping on demand
PDF generation and extraction
Pay only per call from $0.01
One toolbox for AI app workflows
Add scheduling and scraping to an AI app
Generate or process PDFs without managing infrastructure
Automate browser and data workflows on demand
Prototype tools with predictable pay-per-use costs
4.8
6 ratings
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Comments
10This pay-per-call toolbox looks handy for AI app prototyping. Scheduling API, on-demand web scraping and PDF generation/extraction cover many common automation needs, starting at $0.01 per call. My main worry is cost creep for heavy scraping or bulk PDF jobs. It would help to have clearer rate limits, error billing rules, and MCP tool documentation before building core workflows on it.
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its a best tool ever i have used
For indie devs wiring AI agents to external tools, the pay-per-call model is much easier to justify than a monthly subscription - most agent workloads are spiky, not steady. Two things that would make this a no-brainer for me: does the scraping endpoint handle JS-rendered pages (many agent workflows need SPA content), and is there a per-key usage dashboard so I can set spending caps when reselling the workflow to customers? The MCP integration is a smart move - more agent frameworks are standardizing on it.
The combination of scheduling, scraping, and PDF extraction in one pay-per-call toolbox is the useful part for small AI apps that do not want three vendors. I would want to know whether failed scrape jobs still bill, and whether the MCP tool exposes the same endpoints as the HTTP API so an agent does not need a separate integration path.
pay per call use is case is what I love
Building CloudPloy, a deployment platform for AI tools.
The pay-per-use model is a nice fit for AI apps that need occasional infrastructure. The combination of HTTP and MCP access makes the toolbox flexible; curious how teams are choosing between scheduled jobs and on-demand calls.
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pay-per-use model is a nice fit for AI apps that need occasional infrastructure. The combination of HTTP and MCP access makes the toolbox flexible; curious how teams are choosing between scheduled jobs and on-demand calls.
Pay-per-use is the fair model for API utilities that side projects would never justify a subscription for. Curious which of the APIs gets the most calls so far - is it image, PDF, or data utilities?
For the scheduling API, what happens when an AI agent retries a timed-out request? An idempotency key shared by the HTTP and MCP interfaces would help prevent duplicate jobs and charges. It would be useful to show a retry example alongside the per-call pricing.
Pay-per-call is appealing for spiky agent workloads. The combination of HTTP and MCP is interesting—how do you handle retries and idempotency for scheduled jobs so a timeout does not create duplicate work or charges? A small retry example in the docs would help.