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mathematica-skill

mathematica-skill

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mathematica_doctor

Detects installed Wolfram kernels, licenses, frontend, and wolframscript to diagnose environment setup issues before running Wolfram Language computations.

Instructions

检测 Wolfram 内核、许可证、前端和 wolframscript。

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": "mathematica_doctor"
  }
}

Input Schema

NameRequiredDescriptionDefault
frontendNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing: it does not state that the operation is read-only/non-mutating, whether it can hang or take long (probing a license server), or what it reports on failure. Naming the four subsystems it probes is the only real behavioral content.

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?

A single short sentence, front-loaded with the verb and the probed targets, with no filler. It is efficiently structured, though its brevity is partly under-specification rather than true conciseness.

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

Completeness2/5

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

With no annotations, no output schema, and an undocumented parameter, the definition should say what a check run reports (per-subsystem pass/fail?) and what the `frontend` flag does. Neither is present, so an agent cannot predict the result shape or the cost of the call.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain the single `frontend` boolean, and it does not — it only lists 前端 as one of the checked subsystems, leaving the agent to guess that the flag toggles whether the frontend probe runs (and that it defaults to false). That is a meaningful gap on the only input the tool has.

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 gives a specific verb (检测 / inspect) and enumerates the concrete targets: Wolfram kernel, license, frontend, and wolframscript. That is far clearer than the bare name 'doctor', and no sibling tool (plan, lint, build, run, compute, etc.) overlaps with environment diagnostics, so differentiation is implicit rather than stated.

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

Usage Guidelines3/5

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

Usage is only implied: an agent can infer this is the pre-flight/troubleshooting check to run when the environment may be misconfigured. There is no explicit 'use when...', no statement of when it is unnecessary, and no pointer to any related sibling.

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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