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mnbro

aruba-fatturazione-elettronica-mcp

by mnbro

fiscal_document_summary

Read-onlyIdempotent

Summarize a fiscal document into a compact, LLM-friendly overview. Just provide the document ID and direction.

Instructions

Return a compact LLM-friendly summary for one fiscal document.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
documentIdYes
directionYes
confirm_readNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds minimal value by stating 'compact LLM-friendly summary', which hints at concise output but does not contradict annotations. No additional behavioral insights (e.g., rate limits, side effects) are provided.

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

Conciseness3/5

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

The description is a single sentence, which is concise but overly minimal. It states the purpose without any additional context or structure, leaving out important details that could be included in a compact form.

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?

Despite having an output schema, the description is incomplete for an agent to correctly invoke the tool. It lacks parameter semantics and usage guidelines, which are essential given the tool's context among many sibling invoice tools. The description relies entirely on the schema and annotations, which are insufficient.

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

Parameters1/5

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

With 0% schema description coverage, the description fails to explain the meaning of the three parameters (documentId, direction, confirm_read). For example, 'direction' could mean input/output or another concept, but the description offers no clarification.

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 uses a specific verb ('Return') and a clear resource ('compact LLM-friendly summary for one fiscal document'). It distinguishes the tool from siblings like aruba_get_safe_invoice_summary by emphasizing 'compact LLM-friendly', which implies a tailored output for AI consumption.

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?

The description provides no guidance on when to use this tool versus alternatives such as aruba_summarize_invoice or aruba_get_safe_invoice_summary. There is no mention of prerequisites, exclusions, or comparison with similar tools.

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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