generate_threat_modelCreate a threat model from a normalized architecture (ReactFlow nodes + edges). Ingests components via the shared Stage-0 service; the pipeline then auto-generates threats. Returns the diagram id.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Threat model name | |
| edges | No | ReactFlow edges | |
| nodes | Yes | ReactFlow nodes (each: id, position, data.label) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context beyond annotations: it mentions auto-generation of threats, ingestion via shared service, and return of diagram id. Annotations indicate mutation (readOnlyHint=false) and non-destructiveness, which align with creation. 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, each essential: purpose, ingestion method, and return value. No redundant words. Front-loaded with the core action.
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?
The description covers purpose, input, pipeline, and output. Lacks details on error scenarios or prerequisites, but annotations and schema provide some context. Adequate for a creation tool with moderate complexity.
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?
With 100% schema coverage, the schema already documents all parameters. The description adds minimal parameter-specific context (e.g., 'normalized architecture' and 'Stage-0 service'), but this is system-level rather than detailed semantics. Baseline 3 is appropriate.
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 the verb 'Create', the resource 'threat model', and the input 'ReactFlow nodes + edges'. It uniquely identifies this tool among siblings, which focus on other tasks like remediation or compliance.
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?
The description implies usage for generating threat models from architecture data but does not explicitly state when to use or when not to use, nor does it mention alternatives. Sibling tools are distinct enough to avoid confusion, but guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes (e.g., apply vs. get remediation, scan vs. status). A few pairs like get_insights and get_diagram_summary overlap slightly in metrics but descriptions clarify differences. No major ambiguity.
Predominantly verb_noun pattern (apply_, get_, generate_). However, code_review_scan and compliance_scan are noun phrases without a leading verb, and compliance_status is noun_noun, introducing minor inconsistency.
15 tools is at the upper end of typical but still reasonable for a security platform covering scanning, remediation, threat modeling, billing, and reporting. Each tool serves a clear function; no obvious bloat.
Covers core workflows: code/cloud/compliance scanning, threat modeling, remediation (dry-run and apply), traceability, insights. Minor gaps like missing scan listing or account management, but these align with the platform's assessment focus.