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validate_semantics

Read-only

Validate ArchiMate model semantics by detecting invalid relationships, missing references, duplicate names, unused elements, and orphans. Choose summary or full detail for issue reports.

Instructions

Run ArchiMate semantic checks beyond visual reference validation.

Checks include invalid relationship combinations, missing node
references, duplicate element names within the same folder/type,
elements not placed in any view, and orphan service/data elements.

Args:
    detail: `summary` (default) or `full`. The completeness checks
        fire once per element and once per relationship, so a
        mid-build model with no views yet produces one issue per
        concept — 214 issues, ~55 KB, on a 71-element model — of
        which the repeated `code`, `severity` and `message` strings
        are most of the weight. Ask for `full` only when you need
        to read individual issue dicts.

Returns:
    Success envelope with `data.is_valid` (bool),
    `data.issues_count`, `data.issue_counts`, and `data.detail`.

    Under `summary`: `data.issues_by_code` maps each code to
    `{count, severity, ids}`, and `data.errors` carries the
    error-severity issues in full, so `is_valid: false` always
    arrives with its reason. There is deliberately no `data.issues`
    key — read `full` if you want that list.

    Under `full`: `data.issues`, one dict per issue.

Errors:
    `ModelNotFoundError` if no model is active.
    `ModelOperationError` for an unknown `detail` level, with close
    matches in `error.details.suggestions`.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailNosummary

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations declare readOnlyHint=true, and the description adds detailed behavioral traits: specific checks performed, parameter effects, return structure, and error types. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized with clear sections (purpose, checks, args, returns, errors). Every sentence adds value, and it is front-loaded with the 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?

Despite having only one parameter and an existing output schema, the description fully covers the tool's behavior, return values, and errors. It is complete for its complexity and sibling context.

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

Parameters5/5

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

The single parameter detail is thoroughly explained with examples of output size and guidance on choosing summary vs full. This compensates for the 0% schema description coverage.

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 it runs ArchiMate semantic checks beyond visual reference validation, and lists specific check categories. This distinguishes it from sibling tools like validate_model and repair_semantic_issues.

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 provides guidance on when to use the detail parameter (summary vs full) and mentions error conditions, but does not explicitly compare to sibling tools or state when not to use this tool.

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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