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Doctor

doctor

Diagnose the 3D runtime to identify available engines, missing dependencies, and OS-specific fixes, then return copy-pasteable install commands when a backend is unavailable.

Instructions

Diagnose this runtime: OS, Python, which engines exist, what is missing and how to fix it.

Call this when a backend reports itself unavailable, or before telling a user that something cannot be done. It returns copy-pasteable install commands for the current OS rather than a generic error.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "doctor"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.7

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the disclosure burden and does well: it says the output is OS-specific, copy-pasteable install commands rather than a generic error, which tells the agent exactly what value the result has. It does not explicitly confirm the call is side-effect-free or non-mutating, which is the main remaining gap for a no-annotation tool.

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?

Three short sentences with the diagnostic scope front-loaded, followed by the when-to-use trigger and a note on the output style. Every sentence carries distinct information and there is no filler.

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?

The tool takes no parameters and has an output schema, so return values need not be enumerated. The description covers purpose, trigger, and the nature of the returned remediation commands, leaving nothing an agent needs in order to call it correctly.

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?

There are zero parameters, so the baseline is 4. The description still adds meaning by explaining that OS-specificity is detected automatically ("for the current OS"), making clear the caller need not supply environment details.

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?

"Diagnose this runtime: OS, Python, which engines exist, what is missing and how to fix it" states a specific verb (diagnose) plus an enumerated scope of what is inspected and reported. An agent can tell this apart from sibling tools such as list_backends or repair_model without opening any schema.

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?

Gives two concrete triggering conditions: a backend reporting itself unavailable, and before telling a user something cannot be done. That is clear invocation context, though it does not name a sibling alternative or explicitly say when not to use it.

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