Skip to main content
Glama

cad_nlp

Parse natural language CAD requests into tool calls, or continue a multi-turn chat that resolves references like 'move it'. Maps free-form English/Chinese text to CAD commands, returning arguments or suggestions when ambiguous.

Instructions

Parse a natural language request into a tool call or continue a chat.

聚合 NLP 操作。按 ``action`` 派发:command / chat。
- ``command``: map free-form English/Chinese text to a CAD tool call
  (returns ``tool`` + ``arguments``; does NOT execute it). Ambiguous
  requests return candidate ``suggestions``.
- ``chat``: multi-turn dialogue with anaphora resolution — a create
  intent executes immediately and its object is remembered so "move it"
  / "把它" resolve to that object.

When not to use: ``cad_nlp`` is a convenience dispatcher. For
deterministic, schema-driven control prefer calling the concrete
aggregate tools directly (``cad_object``, ``cad_file``, ...). ``command``
only parses — you must dispatch the returned call yourself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nlpYesNLP operation to perform, discriminated by `action`: command or chat.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolNoResolved tool name
actionYesNLP action executed
intentNoParsed intent / rule name
messageNoStatus description
responseNoChat response text
argumentsNoResolved arguments
confidenceNoMatch confidence
suggestionsNoCandidate intents
Behavior5/5

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

Annotations provide no safety hints (all false), so the description carries the full burden. It discloses critical behaviors: command does NOT execute the tool call, ambiguous requests return suggestions, and chat creates objects immediately and remembers them for anaphora resolution. This goes well beyond the annotations and fully informs the agent of side effects and return semantics.

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 well-structured with bullets and front-loaded with the main purpose. However, the Chinese sentence '聚合 NLP 操作。按 ``action`` 派发:command / chat。' essentially repeats the opening English sentence, adding mild redundancy. Otherwise, every sentence earns its place.

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?

Given the tool's complexity (two modes, anaphora, parsing vs execution), the description covers all key aspects: when to use, what each mode does, return behavior, and exclusions. With an output schema present, no return format details are needed. The description is complete for an agent to select and invoke the tool correctly.

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 essential semantics: it explains the action discriminator's meaning, what command returns (tool + arguments, no execution), and how chat handles anaphora. This is practical, actionable information that the schema's property descriptions do not provide, significantly enhancing correct invocation.

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 clearly states a specific verb+resource: 'Parse a natural language request into a tool call or continue a chat.' It further distinguishes between command and chat modes, and positions the tool as a convenience dispatcher relative to concrete sibling tools like cad_object and cad_file. This makes its purpose unambiguous.

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?

The description provides explicit when-not-to-use guidance: 'For deterministic, schema-driven control prefer calling the concrete aggregate tools directly (cad_object, cad_file, ...).' It also clarifies that command only parses and requires the agent to dispatch the returned call, giving clear direction on when to use this tool versus alternatives.

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/Tianshang301/TianshangCAD'

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