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BrokerBusiness-ai

skan-zgodnosci-mcp

get_questionnaire

Retrieve a complete questionnaire for information security and GDPR/NIS2 compliance screening, with 30 scored questions plus optional needs-based questions, filterable by area.

Instructions

Pelna lista pytan skanu: 30 pytan punktowanych i 12 pytan o potrzeby.

Argumenty: obszar: opcjonalny identyfikator obszaru (inwentaryzacja, dostep, kopie, incydent, rodo, dostawcy, ludzie, technika). Bez niego zwracane sa wszystkie. z_potrzebami: czy dolaczyc 12 pytan poza punktacja, ktore sluza do rekomendacji.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
obszarNo
z_potrzebamiNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are present, so the description carries the behavioral disclosure burden. It states that the tool returns a full list and how parameters modify that list, but it does not mention permissions, read-only status, or error behavior. For a simple retrieval tool this is adequate but not rich.

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 compact and well-structured: one opening line defines the resource, then an 'Argumenty' section explains each parameter. Every sentence contributes useful information, and there is no filler or redundancy.

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

Completeness4/5

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

The description covers the tool's two optional arguments and the composition of the returned questionnaire, and an output schema exists to define response structure. It does not differentiate from siblings or describe error handling, but nothing needed to make a correct basic call is missing.

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

Parameters5/5

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

Schema description coverage is 0%, but the description documents both parameters thoroughly. It enumerates the accepted values for 'obszar' and explains the semantic effect of omitting it, and it clarifies the role of 'z_potrzebami' in returning recommendation questions. This fully compensates for the lack of schema-side descriptions.

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 clearly identifies the tool as returning the full questionnaire list, specifying exact composition: 30 scored questions and 12 needs questions. It is concrete about the resource and scope, though it does not explicitly contrast itself with sibling tools.

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?

It gives clear context for using the optional 'obszar' argument, lists the accepted area identifiers, and explains that omitting it returns everything. The 'z_potrzebami' flag is also explained. It does not mention when to use a sibling tool instead, so it stops short of full alternative guidance.

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