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ai-act-mcp

ai-act-mcp

by a2welt
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

check_obligations

Retrieve EU AI Act obligations for your AI system based on risk tier and role, with article citations. Specify if it's a general-purpose AI model.

Instructions

List the concrete EU AI Act obligations for a given risk tier and role (provider or deployer), with article citations. Set is_gpai true if you also provide a general-purpose AI model.

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": {
      "tier": "<tier>"
    },
    "name": "check_obligations"
  }
}

Input Schema

NameRequiredDescriptionDefault
tierYes
roleNoprovider
is_gpaiNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations provided; the description carries full burden. It describes the tool's behavior as a list operation with citations, which is transparent enough. No destructive or side effects mentioned, but not required for a query 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?

Two sentences, front-loaded with the verb 'List', no unnecessary words. Every sentence adds value.

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 input parameters well but does not describe the output format beyond mentioning article citations. Given no output schema, it is adequate but could be slightly more complete.

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

Parameters4/5

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

Schema has 0% description coverage, so the description adds value by explaining each parameter's purpose: tier and role for obligations, and is_gpai for general-purpose AI. It adds meaning beyond enum names.

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?

Description clearly states it lists concrete EU AI Act obligations based on risk tier and role, with article citations. It distinguishes from siblings like classify_risk (classification) and next_deadlines (deadlines) by focusing on obligations.

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 on when to use: specify risk tier, role, and optionally is_gpai for general-purpose AI. It does not explicitly state when not to use or provide alternatives, but the usage is implied.

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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