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gpt2nndk

AutoCAD MCP Server

by gpt2nndk

annotation

Create text, dimensions, and leaders in AutoCAD drawings. Supports linear, aligned, angular, and radius dimensions.

Instructions

Annotation: text, dimensions, and leaders.

Operations: create_text — data: {x, y, text, height?, rotation?, layer?} create_dimension_linear — data: {x1, y1, x2, y2, dim_x, dim_y} create_dimension_aligned — data: {x1, y1, x2, y2, offset} create_dimension_angular — data: {cx, cy, x1, y1, x2, y2} create_dimension_radius — data: {cx, cy, radius, angle} create_leader — data: {points: [[x,y],...], text}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
operationYes
include_screenshotNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

A4.1/5.0
Behavior2/5

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

The annotations already mark the tool as non-read-only, and the operation names imply creation, but the description adds no behavioral context beyond that. It does not mention side effects on the drawing, error behavior, coordinate-system assumptions, or what happens after a successful operation.

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 compact, well-structured as a scannable operation list, and every line adds useful information. There is no filler or redundant restating of the schema.

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 generic schema, the description is nearly complete: it covers all operations and their payload shapes. Minor gaps include undocumented behavior of include_screenshot and lack of explicit units or coordinate context, but these do not prevent correct operation selection.

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 input schema is generic and has 0% schema description coverage, so the description carries the full burden. It compensates thoroughly by defining the exact data object shape expected for every operation, including required fields and optional markers.

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 names the resource ('Annotation') and enumerates six specific creation operations (text, dimension variants, leader), so an agent knows exactly what the tool does. It is clearly distinguishable from sibling tools like drawing, entity, and layer by its scope.

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 opening line 'Annotation: text, dimensions, and leaders' plus the operation list gives a clear context for when to use this tool. It does not explicitly name alternatives or exclusions, but the intended usage is obvious enough for an agent to select it appropriately.

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

Deploy Server

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