Skip to main content
Glama
mnbro

aruba-fatturazione-elettronica-mcp

by mnbro

aruba_answer_invoice_question

Read-onlyIdempotent

Collects structured invoice data to answer natural-language questions about electronic invoices, including filtering by date and direction.

Instructions

Collect structured context for an LLM to answer a natural-language invoice question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYes
date_fromNo
date_toNo
directionNoboth
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

The description's phrase 'Collect structured context' implies a read-only operation, which aligns with annotations (readOnlyHint=true, destructiveHint=false, idempotentHint=true). However, it adds no behavioral details beyond what the annotations already convey, such as side effects or authorization needs.

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?

The description is a single sentence, front-loaded with the main action. It is concise and free of superfluous words. However, it sacrifices essential detail for brevity.

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?

Given the tool's complexity (5 parameters, many siblings, no parameter descriptions) and the existence of an output schema (not provided), the description is incomplete. It omits what the structured context looks like, how the question is used, and does not guide the agent on expected inputs or outputs.

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

Parameters2/5

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

Schema description coverage is 0%, meaning no parameter descriptions exist in the schema. The description fails to explain any parameters, including the crucial 'question' parameter and filters like date_from, date_to, direction, and limit. It does not compensate for the missing schema documentation.

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?

The description states 'Collect structured context for an LLM to answer a natural-language invoice question,' which clearly indicates the tool's purpose of gathering context for answering invoice questions. However, it does not differentiate from sibling tools like aruba_get_invoice_full_context or aruba_search_invoices that also retrieve invoice data.

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, nor does it mention when not to use it or any prerequisites. With many sibling tools performing similar retrieval tasks, an agent lacks direction for choosing this tool.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mnbro/aruba-fatturazione-elettronica-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server