When developers compare receipt OCR APIs, they usually look at accuracy, pricing, and format support. Those still matter. But in 2026, there's a new dimension that's increasingly decisive: AI agent readiness.
Can an AI agent discover and use the API without human setup? Is there an MCP server? llms.txt? A proper OpenAPI spec? Agent-optimized documentation?
Here's how the major players stack up.
The APIs compared
- ReceiptConverter — receiptconverter.com
- Veryfi — veryfi.com
- Mindee — mindee.com
All three handle receipt and invoice extraction. All three have REST APIs. The differences show up in how they approach the AI-native developer workflow.
AI agent readiness scorecard
| Feature | ReceiptConverter | Veryfi | Mindee |
|---|---|---|---|
| MCP server (npm) | ✅ receiptconverter-mcp | ❌ | ❌ |
llms.txt | ✅ | ❌ | ❌ |
llms-full.txt | ✅ | ❌ | ❌ |
AGENTS.md | ✅ | ❌ | ❌ |
| OpenAPI spec (public) | ✅ /api/v1/openapi.json | Partial | Partial |
| MCP directory listed | ✅ | ❌ | ❌ |
| BreadcrumbList JSON-LD | ✅ | ❌ | ❌ |
| BlogPosting schema | ✅ | ❌ | ❌ |
| AI crawlers allowed | ✅ (explicit) | Default | Default |
What each feature means in practice
MCP server
An MCP server means AI clients like Claude Desktop, Cursor, and Windsurf can use the API as a native tool — no custom HTTP code required. The user adds a config block and restarts their client.
Without an MCP server, an AI agent has to discover the API, figure out the auth format, write the HTTP call itself, and handle errors — every time it needs to use it.
ReceiptConverter is currently the only receipt OCR API with a published, maintained MCP server.
llms.txt and AGENTS.md
These files are analogous to robots.txt but for AI agents. llms.txt gives an LLM a concise overview of what the service does and how to use it. AGENTS.md provides agent-specific capabilities, endpoint details, and usage examples in a format optimized for autonomous consumption.
When an AI agent (or a developer using an AI assistant) asks "how do I use the ReceiptConverter API?", the LLM can answer correctly and completely because these files exist. With Veryfi and Mindee, the LLM has to rely on training data that may be outdated or incomplete.
OpenAPI spec
A public, machine-readable OpenAPI spec at a stable URL means frameworks like LangChain, AutoGPT, and Google ADK can auto-generate working integrations. It also means developers can import the API into Postman, Insomnia, or any API client in one click.
Pricing comparison
| Plan | ReceiptConverter | Veryfi | Mindee |
|---|---|---|---|
| Free tier | 5/month (no account) | 500 documents trial | 250 pages/month |
| Entry paid | $9/month (100 receipts) | ~$0.08/document | ~$0.03/page |
| API access | All paid plans | All plans | All plans |
Veryfi and Mindee have more flexible pay-per-use pricing for high volumes. ReceiptConverter's flat monthly pricing is simpler for predictable workloads.
Accuracy and format support
All three APIs use AI-based extraction and handle the common cases (JPG, PNG, PDF) well. Veryfi and Mindee have been around longer and have processed more receipts — their accuracy on edge cases (thermal paper, faded text, handwritten amounts) may be higher.
ReceiptConverter's strength is in the developer experience: simpler API, cleaner response schema, and the AI-native tooling described above.
The bottom line
If you're building an AI agent or using Claude/Cursor: ReceiptConverter is the only option with an MCP server and AI discovery files. The integration path is dramatically shorter.
If you need enterprise SLAs, high volume, or specific compliance requirements: Veryfi and Mindee have more mature enterprise offerings.
If you're optimizing for cost at scale: Compare per-unit pricing across all three at your expected volume. Mindee's page-based pricing can be cheaper for high-volume document processing.
Get started with ReceiptConverter: docs/quickstart · MCP server · Pricing