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Pennylane: Import a supplier invoice

pennylane_import_supplier_invoice

Pennylane: Upload a supplier invoice PDF with its date, due date and amounts. Uploads the PDF and creates the supplier invoice, which then appears under Invoicing > Supplier Invoices and posts entries to the Purchases journal. Only PDF files work, up to 100 MB. Pennylane rejects a PDF that already exists in the account, so importing the same file twice fails. Amounts are strings and the lines must add up to the total. It books an expense in your accounts, so it asks for your OK. By default you confirm each call; you can switch that off for this tool. Reaches other people: confirm with the user before each call. Needs the user's Cotlet API key with access to this app.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taxYesTotal tax, e.g. 12.40.
dateYesInvoice date printed on the PDF.
amountYesTotal including tax; must equal amount_before_tax plus tax.
deadlineYesPayment due date.
file_urlYesHTTPS link to the PDF, or a PDF sent with the request.
supplier_idYesSupplier ID from list_suppliers.
amount_before_taxYesTotal before tax, e.g. 62.00.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Goes well beyond the annotations, disclosing file-type and size limits (PDF only, up to 100 MB), duplicate-file rejection, that amounts are strings, that lines must sum to the total, the accounting side effects (expense booked, Purchases journal), confirmation/override behavior, and the required Cotlet API key with app access. This is exactly the behavioral context an agent needs for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads purpose then constraints, and nearly every sentence carries information. Slightly repetitive on the confirmation topic (three sentences touch on user approval), but nothing is wasted enough to hurt readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 7-required-parameter mutation with no output schema, the description covers purpose, constraints, side effects, confirmation policy and auth needs, leaving no material gap an agent would need before invoking it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is already 100%, so the baseline is 3; the description adds a cross-field validation rule ('the lines must add up to the total') and clarifies that amounts are strings, exceeding what the schema states per-field.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Upload/Import) and resource (supplier invoice PDF) and describes the downstream effect precisely: the invoice appears under Invoicing > Supplier Invoices and posts entries to the Purchases journal. This clearly distinguishes it from siblings like pennylane_list_supplier_invoices and pennylane_create_customer_invoice.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear contextual guidance: it books an expense, asks for the user's OK, defaults to confirming each call, and reaches other people so the user must be consulted before each call. It does not name alternative sibling tools or state explicit when-not conditions, but the usage context is well established.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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