Skip to main content
Glama
U-C4N
by U-C4N

List Entities

entity_list
Read-only

List AutoCAD entities with optional type and layer filters, returning handles, properties, and pagination metadata. Compact mode reduces payload size and reports total, truncated, and next offset.

Instructions

List entities in the drawing with optional type and layer filters.

Returns handle, type, layer, color, and type-specific properties. Use handles with entity_get, entity_move, entity_delete, etc.

This is the most expensive result on the server — the full record runs ~250 characters per entity, and properties.bounding_box alone is about a third of it. When all you need is handles, say so::

entity_list(layer_filter="GEOMETRY", fields=["handle", "type"], compact=True)

Paging honesty: a plain list has nowhere to say that more entities followed the page, so compact=True is the only mode that reports total, truncated and next_offset — all measured against the same filters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum entities to return
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.
offsetNoNumber of entities to skip
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.
type_filterNoFilter by entity type: LINE, CIRCLE, ARC, LWPOLYLINE, TEXT, MTEXT, INSERT, HATCH, etc.
layer_filterNoFilter by layer name

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond the annotations' readOnlyHint, the description discloses significant behavioral traits: it warns that this is the most expensive server result, quantifies payload size, and honestly explains that pagination truncation is only reported in compact mode. This is rich context that helps the agent anticipate performance and response shape.

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-structured and front-loaded: purpose first, then return details, then a concise cost/performance warning with an example, followed by paging honesty. Every sentence adds relevant information, and the code block improves readability without bloating the text.

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 tool with 6 parameters and an output schema, the description covers the core behavior, return values, performance trade-offs, and edge cases (truncation) thoroughly. It also implies the output shape via the return mention and the compact envelope. Given the output schema exists, this is complete enough for an AI agent to select and invoke confidently.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by demonstrating parameter use in an example (fields and compact) and clarifying that compact is the only mode reporting total/truncated/next_offset. It does not fully explain all type-specific properties, but the schema already covers each parameter's purpose.

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 begins with 'List entities in the drawing with optional type and layer filters,' clearly stating the verb, resource, and scope. It also enumerates the return fields (handle, type, layer, color, and type-specific properties), distinguishing it from other entity tools like entity_get or entity_delete.

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 clear practical usage context: it advises using handles for subsequent operations and gives a concrete example with filters and compact mode to optimize performance. However, it does not explicitly mention when not to use this tool or contrast it with alternative listing tools (e.g., layer_list, selection_get).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/U-C4N/Autocad-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server