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pablixnieto2

ETL-D MCP Server

by pablixnieto2

extract_invoice_v1_documents_invoice_post

Extracts invoice fields from PDF files and returns structured JSON, enabling automated data integration and analysis.

Instructions

Process a PDF invoice and return structured JSON.

Cost: 10 credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pdf_urlNoURL to the PDF file
pdf_base64NoBase64 encoded PDF string
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the credit cost (10 credits) and the output format (structured JSON), which is useful. However, it does not mention potential side effects, error handling, or whether the operation is read-only or destructive. For a processing tool, this gap is moderate.

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?

The description is extremely concise (two sentences) and front-loaded with the primary purpose. It also includes the credit cost, which is practical information. There is zero superfluous text, making it highly efficient.

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

Completeness2/5

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

Although the tool is simple, the description fails to mention that at least one of pdf_url or pdf_base64 is required (both are optional in the schema). It also does not provide any details about the structure of the returned JSON or any potential prerequisites. This leaves essential usage conditions uncovered, making it incomplete for an agent to call correctly without additional assumptions.

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?

The schema provides 100% coverage with descriptions for both parameters (pdf_url and pdf_base64). The tool description adds no additional meaning beyond what the schema already conveys, such as how to choose between the two or whether one is required. With full schema coverage, the baseline of 3 is appropriate.

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?

The description states a specific verb+resource ('Process a PDF invoice') and a clear result ('return structured JSON'). This distinguishes it from siblings like extract_resume or extract_custom, which target different document types. The purpose is immediately obvious and unambiguous.

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?

The description gives clear context that this tool is for processing invoice PDFs, which implies when to use it. However, it does not explicitly mention exclusions or alternatives (e.g., use extract_custom for custom schemas). It relies on the tool name and resource type, so it is context-rich but lacks explicit 'when not to use' guidance.

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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