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Ansvar: EU Compliance & Legal Intelligence

Create Dfd

create_dfd

Validate a DFD artifact and render it as styled Mermaid. Returns {mermaid, validation_errors, structural_warnings}. Use after the DFD specialist (/threat-modeler-dfd) has finished extraction so the graph integrity (valid node types, declared trust_zones, reachable edge endpoints, recognised regulatory tokens) is checked before the artifact is submitted via submit_response on scoping.component_identification. artifact = {nodes, edges, trust_zones, assets}, each a list. node: {id, type, trust_zone, name?} where type is one of process|data_store|external_entity|actor and trust_zone references a trust_zones[].id. edge: {src_node, dst_node, id?, protocol?, authentication?, encrypted?, crosses_boundary?} where src_node/dst_node reference node ids (from/to accepted as aliases). trust_zone: {id, name?}. asset: {owner_node, id?, regulatory_relevance?} where owner_node references a node id and regulatory_relevance tokens are one of GDPR|PCI_DSS|DORA|NIS2|EU_AI_Act|HIPAA|ePrivacy|EBA|EIOPA. Bad input returns validation_errors with mermaid=null; it never raises.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
artifactYesThe DFD to validate and render: {nodes, edges, trust_zones, assets}, each a list. node = {id, type, trust_zone, name?} where type is process, data_store, external_entity, or actor and trust_zone references a trust_zones[].id. edge = {src_node, dst_node, id?, protocol?, authentication?, encrypted?, crosses_boundary?} where src_node and dst_node reference node ids (from/to are accepted aliases). trust_zone = {id, name?}. asset = {owner_node, id?, regulatory_relevance?} where regulatory_relevance tokens are GDPR, PCI_DSS, DORA, NIS2, EU_AI_Act, HIPAA, ePrivacy, EBA, or EIOPA.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / artifact / description
      Added value: +"The DFD to validate and render: {nodes, edges, trust_zones, assets}, each a list. node = {id, type, trust_zone, name?} where type is process, data_store, external_entity, or actor and trust_zone references a trust_zones[].id. edge = {src_node, dst_node, id?, protocol?, authentication?, encrypted?, crosses_boundary?} where src_node and dst_node reference node ids (from/to are accepted aliases). trust_zone = {id, name?}. asset = {owner_node, id?, regulatory_relevance?} where regulatory_relevance tokens are GDPR, PCI_DSS, DORA, NIS2, EU_AI_Act, HIPAA, ePrivacy, EBA, or EIOPA."
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Adds useful behavioral details beyond annotations: returns {mermaid, validation_errors, structural_warnings}, 'Bad input returns validation_errors with mermaid=null; it never raises', and lists validation checks. It does not disclose side effects despite readOnlyHint=false, but no direct contradiction arises.

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 long but dense, covering purpose, workflow, input structure, and error handling in a structured way. It front-loads the core action and uses the rest for necessary detail, so it is efficient despite length.

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

Completeness5/5

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

For a complex tool with nested objects, the description covers workflow context, input schema details, return values, and error behavior. It leaves no significant gaps for an agent to select and invoke the tool correctly.

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

Parameters3/5

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

Schema description coverage is 100% and the description mirrors the schema's artifact structure almost exactly. It adds no new parameter semantics beyond what the schema already provides, so the baseline 3 is appropriate.

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 opens with 'Validate a DFD artifact and render it as styled Mermaid,' which is a specific verb+resource. It clearly distinguishes itself from siblings by referencing the DFD specialist workflow and submit_response, positioning it as the validation/rendering step.

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?

Explicitly states when to use: 'after the DFD specialist has finished extraction' and 'before the artifact is submitted via submit_response'. This gives clear workflow context. However, it does not name alternative tools or explicitly state when not to use it, so it falls short of a 5.

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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