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by byrondelgado
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validate_model

Read-only

Check visual references in your ArchiMate model for invalid connections and nodes, returning validity status and counts of defects.

Instructions

Validate visual references in the active model.

Delegates to pyArchimate's `check_invalid_conn` and
`check_invalid_nodes` helpers. Diagram-only annotation connectors
(a note line joining an Archi Note to an element) are excluded: they
have no backing relationship by design, so they are not defects. A
connector between two element-backed nodes whose relationship is
genuinely missing is still reported. Use `validate_semantics` for
ArchiMate semantic checks beyond visual references.

Returns:
    Success envelope with `data.is_valid` (bool),
    `data.invalid_connection_ids`, `data.invalid_node_ids`,
    `data.invalid_connections_count`, and
    `data.invalid_nodes_count`.

Errors:
    `ModelNotFoundError` if no model is active.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.9/5.0
Behavior5/5

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

Discloses delegation to pyArchimate helpers, exclusion of certain connectors, and error conditions (ModelNotFoundError). Annotations already mark it read-only; the description adds meaningful behavioral context beyond that.

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?

Structured into clear sections: purpose, delegation details, return data, errors. Every sentence is essential, no filler. Front-loaded with main purpose.

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?

Given no parameters and a rich output schema, the description covers purpose, usage guidelines, behavioral details, return structure, and errors. It is fully sufficient for an agent to decide when and how to use this tool.

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?

No parameters exist, so the description cannot add parameter semantics. Baseline is 4 for zero-parameter tools. The description compensates by detailing return fields, which adds value beyond schema.

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 clearly states the tool validates visual references in the active model, specifying it checks invalid connections and nodes. It distinguishes itself from the sibling 'validate_semantics' by clarifying one is for visual references and the other for semantic checks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use (for visual references) and when not to (use validate_semantics for semantic checks). Also clarifies that diagram-only annotation connectors are excluded from defect reporting, giving clear guidance on what is and isn't checked.

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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