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CaeliaEve

AutoCAD MCP Ultra

by CaeliaEve

Entity: Smart Select (semantic predicate)

entity_select_smart
Read-only

Select AutoCAD entities by semantic predicates like type, layer, or proximity instead of memorizing handles. Returns matching records, typically handles, ready for downstream dimensioning.

Instructions

Select entities by semantic predicate instead of memorising handles.

Uncapped. The usual next step is dimension_auto(handles), so fields=["handle"] is normally all this needs to return.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoProject to these fields, in this order (e.g. ['handle','type','layer']); 'properties.<key>' reaches one nested value. Omit for the full record; an unknown name errors and lists the valid ones.
compactNoReturn a columnar {fields, rows, count, offset, total, truncated, next_offset} envelope instead of dicts: much cheaper per row, and the only shape that reports truncation.
predicateYesPredicate dict (all keys optional, AND-ed): type (e.g. 'LINE'), layer (name), near ([x,y,radius]), length_range ([min,max], LINE/ARC only), color (ACI int).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds the useful 'Uncapped' behavior and the workflow hint about returning handles, which goes slightly beyond the annotations and schema. It does not describe pagination or truncation behavior, though the compact parameter and output schema already carry some of that information.

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 two short sentences with no filler. The core purpose is stated first, and the second sentence provides actionable workflow guidance. 'Uncapped' is terse but meaningful behavior information.

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

Completeness4/5

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

Given the rich input schema, output schema presence, and readOnlyHint annotation, the description covers the essential selection purpose and common next step. It lacks explicit routing among the many selection-related sibling tools, but an agent can invoke this tool correctly using the schema plus the description's guidance.

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?

Schema description coverage is 100%, and the schema itself documents predicate, fields, and compact in detail. The description adds practical guidance beyond the schema by recommending fields=['handle'] for the typical dimension_auto workflow, which helps the agent choose parameter values more effectively.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Select entities') and the method ('by semantic predicate instead of memorising handles'). This clearly distinguishes it from handle-based entity access, though it does not explicitly differentiate it from sibling tools like analysis_select_by_type or analysis_select_by_layer.

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

Usage Guidelines3/5

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

The phrase 'instead of memorising handles' implies use when the caller has semantic criteria rather than known handles, and 'the usual next step is dimension_auto(handles)' provides concrete workflow context. However, it does not explicitly state when to prefer this over selection/analysis siblings, nor mention any exclusions.

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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