OCR Web Service

@OCR Web Service extract all text from this scanned invoice PDF and return it as plain text so I can parse the line items.

The OCR Web Service processed the invoice and returned the extracted text: 'INVOICE #4521 | Date: 2024-01-15 | Customer: Acme Corp | Item: Widget A - $120.00 | Item: Widget B - $85.50 | Subtotal: $205.50 | Tax: $16.44 | Total: $221.94' The service also provided word-level coordinates for each text element, which could be used to reconstruct layout or validate field positions. Processing consumed 1 page from your account balance.

This example shows the core OCR capability: converting image-based documents into machine-readable text. Before running, check your remaining page balance via the account info tool—each OCR operation consumes pages from your subscription quota. The service returns raw text; you'll need additional parsing logic to structure invoice fields.

@OCR Web Service check my account status and tell me how many pages I have left this month before I submit this batch of 50 receipts.

Your OCR Web Service account shows: - Subscription Plan: Professional - Remaining Pages: 1,247 of 2,000 - Expiration Date: 2024-12-31 You have sufficient quota to process all 50 receipts (each receipt = 1 page). After this batch, you'll have approximately 1,197 pages remaining for the rest of the billing period.

This example demonstrates proactive quota management—essential for batch workflows. The account info tool prevents mid-job failures due to exhausted page limits. Always check remaining pages before large OCR runs, especially if you're processing documents programmatically or on a schedule.

@OCR Web Service pull the processing logs from the last 7 days and summarize any failed OCR jobs so I can troubleshoot the errors.

Retrieved 23 OCR jobs from the past week. Three jobs failed: 1. Job ID 8821 (Jan 10): Error 'Unsupported image format' — file was a TIFF with CMYK color space, which requires RGB conversion before submission. 2. Job ID 8847 (Jan 12): Error 'Image resolution too low' — source was 72 DPI; service requires minimum 150 DPI for reliable text extraction. 3. Job ID 8903 (Jan 14): Error 'Authentication token expired' — OAuth2 token needs refresh. All other jobs completed successfully with average processing time of 2.3 seconds per page.

This example pairs the log retrieval tool with AI reasoning to surface actionable insights from raw processing history. The logs include error codes and timestamps but require interpretation—having the AI summarize failure patterns saves manual log review. Note that logs are date-scoped; specify your range to avoid retrieving excessive history.

When OCR Web Service fits monthly expense reconciliation

A 5-person finance team reconciling 200-400 vendor invoices per month hits the sweet spot for this MCP. The OAuth2 flow means your team lead sets up credentials once, then anyone in the Switchy workspace can run OCR on scanned PDFs without touching API keys. The recognize tool extracts line items and totals, the account info tool confirms you have pages left before batch runs, and the log tool audits which invoices were processed when. This works until you cross 1,000 invoices per month or need real-time processing—at that scale, you want a dedicated document AI pipeline, not a shared workspace tool. For quarterly close cycles where humans review every extraction anyway, this MCP keeps the workflow in one place without building infrastructure.

Why this MCP struggles with high-volume support queues

A 3-person support team handling 50 tickets per day with screenshot attachments will find this MCP too slow for the job. The recognize tool requires a full SOAP call per image, and the account info check adds latency before each batch. If your SLA is under 2 hours and half your tickets include images, you need OCR baked into your ticketing system, not a workspace add-on. This MCP makes sense for a different scenario: weekly triage of escalated cases where a support lead manually reviews 10-15 complex attachments and needs text extraction to search past tickets. The log tool helps track which images were processed during root-cause analysis. For real-time queues, skip this and use a native integration in your helpdesk.

When this MCP works for one-off contract audits

A 2-person legal ops team auditing 30 vendor contracts per quarter can use this MCP to pull termination clauses and renewal dates from scanned PDFs. The recognize tool outputs word coordinates, so you can locate specific sections without re-reading every page. The account information tool confirms your subscription covers the batch before you start, and the log tool creates an audit trail for compliance. This breaks down if you need structured data extraction—the MCP returns raw text and coordinates, not parsed fields like 'effective date' or 'liability cap'. For quarterly audits where a human reviews every result, this MCP saves 4-6 hours of manual transcription. For ongoing contract lifecycle management, you need a CLM platform with native OCR, not a workspace tool.