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rechnungsapi-mcp

rechnungsapi-mcp

Official
by Rechnungsapi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Analyze a PDF/scanned invoice

analyze_pdf_invoice

Extract structured invoice JSON from scanned or digital PDF, PNG, JPEG, or TIFF files to automate invoice data capture; optionally include individual line items.

Instructions

Extract structured invoice JSON from a scanned or digital PDF/PNG/JPEG/TIFF invoice using the high-accuracy analyzer.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "pdfBase64": "<pdfBase64>"
    },
    "name": "analyze_pdf_invoice"
  }
}

Input Schema

NameRequiredDescriptionDefault
pdfBase64YesBase64-encoded PDF, PNG, JPEG, or TIFF invoice
withLineItemsNoAlso extract individual line items

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations exist, so the description carries the full behavioral burden, yet it only offers the vague quality claim 'high-accuracy analyzer.' It does not state that this is a read-only extraction, whether processing is synchronous or may take time, any size/rate limits, or what happens on unparseable input — all relevant for an OCR-style tool.

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

Conciseness5/5

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

A single front-loaded sentence with the verb and output first, followed by input formats. No filler, no redundant restatement of the tool name.

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

Completeness3/5

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

For a 2-parameter extraction tool with 100% schema coverage this is roughly minimally adequate, but with no output schema the description should say more about the shape of the returned invoice JSON, and with no annotations it should clarify safety and the sync-vs-async choice among its many siblings.

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

Parameters3/5

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

Schema description coverage is 100%, so both pdfBase64 and withLineItems are already documented in the schema, establishing a baseline of 3. The description adds nothing beyond what the schema provides (it repeats the supported formats already in the pdfBase64 description).

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

Purpose4/5

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

States a specific verb+resource ('Extract structured invoice JSON') and enumerates accepted input formats (PDF/PNG/JPEG/TIFF), so the agent knows exactly what the tool produces and consumes. It does not distinguish itself from the sibling async variants (analyze_pdf_invoice_async_submit/status) or from create_zugferd_from_pdf, so it stays at 4.

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

Usage Guidelines2/5

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

No when-to-use or when-not-to-use guidance is given. The description never mentions that synchronous analysis is the alternative to the async submit/status siblings, nor when to prefer a create_zugferd_from_pdf path over plain extraction. Usage is left entirely to inference.

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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