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CaeliaEve

AutoCAD MCP Ultra

by CaeliaEve

Batch: Run Tools

cad_batch
Destructive

Execute an ordered sequence of AutoCAD operations in a single round trip, binding earlier results as inputs to later steps for efficient automation.

Instructions

Execute an ordered list of tool calls in ONE round trip.

N calls collapse into one request/response pair, and bind lets a later step reference an earlier step's result so handles never have to be echoed back through the model.

steps=[
  {"tool": "entity_create_line",  "args": {...}, "bind": "edge"},
  {"tool": "point_from_snap",     "args": {"handle": "$edge", "snap": "mid"},
                                  "bind": "mid"},
  {"tool": "entity_create_circle","args": {"cx": "$mid.x", "cy": "$mid.y",
                                           "radius": 4}},
]

Successful steps report only their handle; pass verbose=True for the full result. Anything without a handle is returned whole.

VALIDATION runs first, always: an unknown tool, a schema-invalid argument or a reference no earlier step binds refuses the whole batch before anything executes. on_error governs run-time failures only. dry_run=True returns that validation report and executes nothing.

ERRORS are typed, never text: each failed step carries error.kind - one of unsupported (with the backend capability), invalid_args, refused, failed, unknown_tool, unresolved_ref, denied, malformed_step.

ATOMICITY is reported, not assumed. Read the atomicity block: on the headless backend rollback restores a full document snapshot; on live AutoCAD it sends an UNDO whose landing AutoCAD never confirms. The default on_error="stop" claims nothing and is exact on both.

NOT CALLABLE from a batch: the raw command/LISP escape hatches, and cad_batch itself. Call those directly.

For a few hundred entities of the same kind, entity_batch_create is denser still (no per-step tool name) - and it can be one step of a cad_batch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stepsYesOrdered steps. Each: {'tool': <tool name>, 'args': {...}, 'bind': <optional name>}. 'bind' names this step's result so a later step can reference it as '$name' (its handle), '$name.field' or '$name.list.0'. '$$' is a literal dollar sign.
dry_runNoValidate every step against its tool's real JSON Schema and execute nothing.
verboseNoReturn each step's full result instead of just its handle.
on_errorNostop (default): halt at the first failure, keep what already ran. continue: run every step. rollback: open a checkpoint first and undo on failure - read the returned `atomicity` block for what that is worth on this backend.stop

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Beyond the destructiveHint annotation, the description discloses validation-first execution, typed error kinds, on_error semantics, dry_run behavior, verbose output behavior, and the nuances of atomicity across backends. This is far more than annotations alone provide and sets accurate expectations for side effects.

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 long but every section earns its place given the tool's complexity: example, validation behavior, error model, atomicity caveats, exclusions, and sibling alternative. The core benefit is front-loaded, and the structure is skimmable with clear labels.

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?

With an output schema present, the description correctly focuses on behavior rather than return shapes. It covers success output, verbose mode, validation, dry_run, error typing, atomicity, and exclusions, making it fully actionable for correct invocation in a wide range of scenarios.

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?

Although schema coverage is 100%, the description adds deep semantics for steps, bind references, '$name.field' access, '$$' escaping, dry_run validation behavior, and on_error modes. The worked example makes parameter composition concrete, going well beyond the schema's field descriptions.

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 opens with 'Execute an ordered list of tool calls in ONE round trip,' which names a specific verb, resource, and key benefit. It also distinguishes itself from entity_batch_create by noting that tool is denser for many homogeneous entities, so the purpose is clear and differentiated from siblings.

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

Usage Guidelines5/5

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

Explicit guidance covers when cad_batch is appropriate and when it is not: it states that raw command/LISP escape hatches and cad_batch itself cannot be called from a batch, and recommends entity_batch_create for large homogeneous sets. This gives an agent concrete routing rules.

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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