Receipts & invoices to spreadsheet
Server Details
Receipts and invoices to checked spreadsheet rows. Doubtful numbers are flagged.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 2 tools
The two tools have cleanly separated roles: create_upload only mints a signed URL for large/on-disk files, while extract_documents performs the actual extraction and export. Descriptions explicitly cross-reference each other (pass file_id to extract_documents), leaving no realistic chance of misselection.
Both names are snake_case verb_noun and read consistently (create_upload, extract_documents). Minor quibble: create_upload actually creates an upload URL rather than an upload, so the object noun is slightly imprecise, but the convention is uniform.
Two tools is on the thin side of the typical 3-15 range, but the surface is a tight two-step pipeline (upload large files, then extract) and each tool is clearly earning its place. The upload tool exists specifically to work around the 2-4 MB inline limits, so it is not padding.
Core lifecycle is covered: ingest by inline data, URL, or signed upload, then extraction with statuses, review reasons, duplicate detection, and downloadable CSV/QuickBooks/file-pack exports. Gaps are minor — no tool to retrieve or re-list a prior extraction, check daily quota, or fetch a single document's detail after the 24-hour links expire.
Available Tools
2 toolscreate_uploadAInspect
Get a one-hour signed upload URL for a document or a zip that is too large to send inline (over 2 MB; up to 100 MB here) or that already sits on disk. PUT the raw bytes to upload_url with a Content-Length header, then pass file_id to extract_documents. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| bytes | Yes | Exact size of the file you will upload, in bytes (at most 100 MB) | |
| filename | No | Original file name, used to name the documents |
Output Schema
| Name | Required | Description |
|---|---|---|
| method | Yes | |
| file_id | Yes | |
| max_bytes | Yes | |
| expires_at | Yes | |
| upload_url | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only declare it is a non-read-only, non-idempotent, non-destructive write; the description adds the details that actually matter: the URL expires in one hour, the HTTP method and Content-Length header are required, the call is free, and the resulting file_id feeds extract_documents. The non-idempotent hint is implicitly explained by each call minting a fresh URL.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loads the purpose and TTL, then packs limits, the required protocol steps, and the handoff into two tight sentences with zero filler. Every clause carries operational information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema that covers return values, the description still explains how to consume upload_url, which is the one field an agent must act on. Combined with the annotation-covered mutation profile and full schema, an agent has everything needed to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so both parameters are already documented, and the description largely restates them (exact byte size, 100 MB ceiling). It adds only the trigger threshold of 2 MB that determines when this tool is needed at all, which does not go materially beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Names the exact verb and resource: 'Get a one-hour signed upload URL', then narrows scope to documents/zips that exceed 2 MB or already exist on disk. The size ceiling (100 MB) and the downstream tool (extract_documents) make it unmistakable against its only sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives explicit use conditions (over 2 MB, up to 100 MB, or already on disk) contrasted with the implied alternative of sending inline, plus a full three-step workflow: PUT bytes with Content-Length, then pass file_id to extract_documents. Nothing about selection or sequencing is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract_documentsARead-onlyIdempotentInspect
Read receipts and invoices (JPG, PNG, WebP, HEIC, PDF, or a zip of them; one document per file or PDF; up to 25 documents per call, 10 MB each, PDFs up to 10 pages) into checked numbers. Give each file as file_id (from create_upload), url (public https) or data_base64 (up to 2 MB inline). MCP requests are capped near 4 MB by the SDK, so keep all inline base64 in one call under about 2 MB and use create_upload for more. Returns one entry per document with vendor, date, currency, line items, subtotal, tax, total and payment method, each field marked ok, check or missing, the checks that ran, a status of ready or needs_review with review_reasons, and duplicate_of for a repeated receipt. Money is a decimal string, never a float. Each document also carries printed: its dates and currency as printed, the paths of fields whose text could not be read (printed.unreadable), the paths of fields a second read changed after a failed check (printed.second_look), and the paths of amounts printed with a dotted thousands separator on a rupiah-style receipt ("31.000") that were read as the whole number (printed.thousands; the value is already converted and the field is marked check), and the paths of amounts the model returned as JSON numbers, whose printed digits are lost (printed.number_typed; the field is marked check). Also returns 24-hour download links for the exports you ask for: csv (default), line_items_csv, quickbooks_csv (one currency; set quickbooks_currency and quickbooks_date_format, MM/DD/YYYY or DD/MM/YYYY) and file_pack (a zip of the originals renamed by date, vendor and total, plus documents.csv). Each link lists the documents left out of that file: quickbooks_csv leaves out every document with status needs_review (and ones missing a date or total), with the reason; csv, line_items_csv and file_pack keep every document. Hints: country (ISO 3166-1 alpha-2) and currency (ISO 4217) help with unclear receipts, date_order (DMY or MDY) settles dates like 03/04/2026. Show documents with status needs_review and their review_reasons to the user before using their numbers. Free for now within a daily quota counted in documents (see start.md); price when payments are enabled: $0.01 per document. Use a client timeout of at least 240 s.
| Name | Required | Description | Default |
|---|---|---|---|
| files | Yes | Documents to read. Each item needs exactly one of file_id, url or data_base64 | |
| country | No | Country of the documents, ISO 3166-1 alpha-2 (for example DE) | |
| exports | No | Files to produce, each as a 24-hour download link. Default: csv; [] writes no file | |
| currency | No | Currency to expect when a document does not print one, ISO 4217 (for example EUR) | |
| date_order | No | How all-numeric dates such as 03/04/2026 are written: day first or month first | |
| quickbooks_currency | No | quickbooks_csv: the one currency the file holds (default: the most common one) | |
| quickbooks_date_format | No | quickbooks_csv: how dates are written (default MM/DD/YYYY) |
Output Schema
| Name | Required | Description |
|---|---|---|
| ready | Yes | |
| exports | Yes | |
| skipped | Yes | |
| documents | Yes | |
| needs_review | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, open-world, but the description goes well beyond: quotas and per-document pricing, the 25-document/10 MB/10-page caps, money returned as decimal strings rather than floats, duplicate_of and status/review_reasons semantics, and which documents each export omits. These are real behavioral traits an agent could not infer from the annotations or schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded and dense, but delivered as a single unbroken paragraph of roughly 300 words mixing input rules, return-value diagnostics, export filtering, hints and pricing. Since an output schema already exists, the long enumeration of printed.unreadable/second_look/thousands/number_typed paths is the least essential part and could be much shorter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 7-parameter, high-consequence extraction tool, the description covers what an agent needs: input size limits and the create_upload fallback, timeout expectations, review-before-use guidance, cost/quota, and per-export inclusion rules. Return shape is further backed by the output schema, so nothing critical is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3, and the description adds genuine meaning: country and currency are framed as hints that help with unclear receipts, date_order is explained against the concrete ambiguity of 03/04/2026, and quickbooks_currency is tied to the one-currency constraint of that export. Only filename is left wholly to the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Opens with a specific verb and resource: read receipts and invoices into checked numbers, then names the accepted formats and per-call limits. An agent can immediately tell this is an OCR/extraction tool and distinguish it from the only sibling, create_upload.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly routes input modes (file_id from create_upload, url, or data_base64) and states the threshold at which the agent must switch to create_upload because of the ~4 MB MCP request cap. It also gives operational guidance (timeout of at least 240 s, review needs_review documents with the user before using the numbers) and names the sibling tool to use for larger payloads.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
create_upload - First observed
extract_documents
Related MCP Connectors
Invoice PDFs, scans and photos to Excel, with the math checked: fields, line items and totals.
Messy spreadsheets in, clean checkable tables out. Every result carries its arithmetic proof.
Review invoice records, flag duplicates, validate totals, and prepare spreadsheet-safe CSV.
Convert PDF bank statements into Excel and check that the balances add up. Invoices and tables too.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceDeterministic verification for AI-generated analysis. Reconciliation, consistency and Excel-integrity checks that stop the line when the numbers don't add up.1MIT
- AlicenseAqualityCmaintenanceEnables an AI assistant to read Portuguese invoices and receipts, file them by tax year, and book them into an Excel or Google Sheets workbook. It works out the boxes of the quarterly IVA return, tracks IRS and Segurança Social totals, and reports what documents are still missing.16MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents and finance teams to extract structured fields from messy invoices and audit them before payment, applying seven evidence-backed risk rules that catch total/tax mismatches, duplicate invoices, missing fields, future dates, and round amounts, then returning a 0–100 score with a payment link. Exposes four MCP tools over stdio and Streamable HTTP (spec 2025-11-25) plus a web UI, backed by an optional AWS Bedrock or OpenAI-compatible extraction brain that degrades gracefully to a zero-dependency offline engine.MIT
- AlicenseAqualityAmaintenanceEnables users to review a supplied batch of supplier invoices or vendor bills for repeated references, normalized reference variations, and identical amount-and-date candidates, returning grouped evidence and exact review reasons from local history. Supports saved reports, CSV export, and use with MCP clients such as Claude Desktop, Claude Code, and Cursor.8MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.