invoice-check
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- AlicenseAqualityDmaintenanceExposes a ledger system (invoice queue, duplicate control, VAT register, contractor history, decision journal) as MCP tools for AI agents, enabling accurate invoice processing with deterministic validation.7MIT
- FlicenseNot gradedqualityDmaintenanceEnables AI-powered invoice analysis and reasoning: given invoice fields, it detects missing data, inconsistencies, duplicates, and proposes actions (register, request data, mark duplicate, review) using deterministic rules.-
- AlicenseNot gradedqualityBmaintenanceGives AI agents a safe way to read and check invoices, returning a deterministic APPROVE, REVIEW or STOP decision with every reason, while no tool can pay, approve or change anything.MIT
- AlicenseNot gradedqualityBmaintenanceEnables accounts-payable teams to turn emailed or uploaded invoice PDFs into structured, source-cited fields via Claude, run deterministic arithmetic, date, PO and duplicate checks, and route each invoice to a named human for approval or rejection through scoped bearer tokens. Also supports listing pending approvals and forecasting what falls due in the coming weeks, with per-extraction cost tracking.MIT
- 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
- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to read, validate against the official KoSIT rules (no Java), create, and display XRechnung and ZUGFeRD e-invoices, and export incoming invoices as DATEV posting batches. Running it with a cloud language model sends invoice contents to that provider's service.MIT
TDQS
Scored across 9 tools
The data-lookup tools (get_work_order, get_price, get_rule) and the action tools (check_invoice, check_all, open_dashboard, record_decision) are clearly distinct. The main friction is between list_invoices, read_invoice and check_invoice, which all operate on a single invoice but differ only subtly (browse vs. extract text vs. validate); descriptions do help, though an agent could still reach for read when check was meant.
All names are snake_case verb-first (list_, read_, get_, check_, open_, record_), which gives a strong, readable pattern. Minor deviations: check_all drops the object noun and uses the same verb as check_invoice for a batch-wide action, which slightly blurs the verb semantics.
Nine tools is well within the sweet spot and each maps to a distinct step in the invoice-review workflow: enumerate, inspect, fetch reference data, validate, surface UI, and record the outcome. Nothing appears redundant or bolted on.
The lifecycle is essentially end-to-end: list/read invoices, pull work-order, price and rule context, run single or batch checks, open a review dashboard, and persist decisions with a rich state machine. The notable gap is read-back: there is no tool to list prior decisions.csv entries or pending 'waiting disputes' other than via the dashboard, so an agent cannot inspect history in-band.