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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
Behavior3/5

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

The description goes beyond the readOnlyHint:false annotation by exposing the specific operations and their data shapes, implying mutating behavior. However, it does not disclose side effects, required permissions, or what happens on success/failure, leaving behavioral transparency only partially covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is concise, front-loads the purpose, and organizes operations as a scannable list. It is not overly verbose, though the list structure could be improved with a short introductory verb phrase.

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

Completeness3/5

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

Given the tool has six distinct operations and an output schema, the description covers the operation specifics but lacks broader context: no use-case examples, no prerequisites, and no explanation of when each dimension type is appropriate. The output schema, if present, may cover return values, but the description remains incomplete for a tool with this complexity.

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

Parameters3/5

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

The schema has 0% description coverage, but the description compensates well by detailing the data fields for each operation and implicitly enumerating valid operation values. Missing is any explanation of the include_screenshot parameter, which is a clear gap.

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 clearly states the tool deals with annotations (text, dimensions, leaders) and enumerates specific create operations, so an agent can infer the tool creates annotation objects. However, it does not explicitly distinguish this from sibling tools like entity or drawing, and the opening line is more of a category label than a verb-driven statement.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives, no mention of prerequisites (e.g., open drawing), and no exclusionary context. The description simply lists operations without explaining the appropriate scenarios for each.

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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