Paid EU AI Act compliance reports for agents. $1.50 per call via x402 (USDC on Base) or Stripe.
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Average 4.8/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: eu_ai_act_compliance generates a paid compliance report, while info provides a free usage guide. There is no overlap or confusion.
Both tools use snake_case, but 'info' is generic and not as descriptive as the lengthy 'eu_ai_act_compliance'. Consistency is acceptable given the small set.
Two tools is slightly low but appropriate for a server focused on a single paid report. The info tool is essential for guiding payment, so the count feels reasonable.
The server fully covers its intended purpose: generating a compliance report and providing instructions. There are no obvious gaps for its scope.
eu_ai_act_complianceEU AI Act compliance reportAGenerate a structured EU AI Act compliance report from a system description, model card, and data-flow document. Nothing is silently optional: system_description is always required (min 50 chars); model_card and data_flows each accept substantive content (min 50 chars) or the exact string 'declined' to explicitly opt out; and exactly one of decision_tree_answers (from the free risk classifier, becomes binding evidence) or skip_decision_tree: true must be sent. All of this is validated free of charge before payment. The deterministic Annex III decision tree runs on every call: user-supplied answers are binding, otherwise answers are AI-derived and recorded as such. The tree classification, article citations, obligations, and evidence checklist are returned verbatim in the report JSON, together with exact enforcement-timeline dates and the matched Annex III area from the curated dataset. A staged AI workflow then produces an article-by-article obligation gap analysis, a required-documentation checklist mapped to Annex IV, and a prioritised remediation plan. The report includes a deterministic input_coverage grade (full, partial, or description_only) computed from what was supplied versus declined, the full AI classification with rationale, a programmatic reconciliation check between the tree and the AI risk tier, and a citation audit that flags any implausible article references. Returns markdown plus structured JSON. Every paid call also generates a branded PDF report and returns its download link (valid 72 hours). Price: $1.50 per call, payable by card (Stripe checkout link in the 402 response) or USDC on Base (x402). Full documentation, inputs, and pricing: https://systemprompt.io/tools/eu-ai-act-compliance-report/ — more governance tools and reports at https://systemprompt.io/mcp
| Name | Required | Description | Default |
|---|---|---|---|
| data_flows | Yes | Data-flow description: what data enters the system, where it comes from, where outputs go (minimum 50 characters), or the exact string 'declined' to explicitly decline. Declining lowers the report's input_coverage grade. | |
| model_card | Yes | Model card or equivalent documentation (minimum 50 characters), or the exact string 'declined' to explicitly decline supplying one. Declining lowers the report's input_coverage grade. | |
| skip_decision_tree | No | Set true to explicitly skip supplying decision-tree answers; the tree is then derived by AI and the skip is recorded in input_coverage. Exactly one of decision_tree_answers or skip_decision_tree: true must be sent. | |
| system_description | Yes | Written description of the AI system: what it does, who uses it, what decisions it informs, and the context it operates in. Minimum 50 characters. | |
| decision_tree_answers | No | Answer indices from the free EU AI Act risk classifier decision tree; the resulting classification becomes binding evidence. Required unless skip_decision_tree is true. |
| Name | Required | Description |
|---|---|---|
| artifact | Yes | |
| _metadata | Yes | |
| artifact_id | Yes | |
| mcp_execution_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only and deterministic. Description adds rich context: free validation, decision tree process, AI workflow, output formats, PDF generation, pricing, and result details. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with main purpose, each sentence adds value. Slightly verbose with payment links and URLs, but justified by tool complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all aspects: inputs, process, outputs, pricing, documentation links. Output schema exists, but description still adds value on PDF and markdown. Complete for agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
100% schema coverage; description expands on all parameters: explains 'declined' strings, minimum lengths, mutual exclusivity of decision_tree_answers/skip_decision_tree, and impact on input_coverage grade.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Generate a structured EU AI Act compliance report', specifying verb and resource. It distinguishes from sibling tool 'info' by detailing inputs and outputs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on required vs optional inputs, decision tree requirement, pricing, and validation. Lacks explicit 'when not to use' or comparison with 'info', but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
infoUsage guideAFree usage guide for this server. Explains how the paid report tools work: exact input requirements, per-call pricing, and how to complete payment via x402 (USDC on Base) or Stripe checkout. Costs nothing and never returns a 402. Call this first before any paid tool.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| artifact | Yes | |
| _metadata | Yes | |
| artifact_id | Yes | |
| mcp_execution_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint=true), description adds key behaviors: free, never returns 402, and explains pricing/payment. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with 'Free usage guide', every sentence adds value with no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and existence of output schema, description fully covers purpose, usage, and behavioral guarantees. Complete for this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has zero parameters, so baseline is 4. Description adds no parameter info, but none needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it's a free usage guide for paid report tools, explaining input requirements, pricing, and payment. It distinguishes itself from the sibling tool (eu_ai_act_compliance) by being the prerequisite guide.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to call this first before any paid tool, and notes it costs nothing and never returns a 402. Provides clear when-to-use and context.
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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