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tensorart-mcp

tensorart-mcp

by giacomodiguida
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_job

Check a Tensor.Art job's status and, when download is true, save its generated images locally for review or reuse.

Instructions

Status of a job; with download=True saves its images locally.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "job_id": "<job_id>"
    },
    "name": "get_job"
  }
}

Input Schema

NameRequiredDescriptionDefault
job_idYes
downloadNo

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?

With no annotations, the description carries the full burden. It does disclose one real side effect — download=True writes images to local disk — but says nothing about where files land, whether images exist before the job completes, what status values are returned, or whether the call is otherwise read-only.

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 tight sentence with the core purpose front-loaded before the parameter note. No waste, but the brevity leaves no room for the lifecycle context an agent needs.

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?

For a job-lifecycle polling tool with no annotations and no output schema, the description should explain return shape or states (pending/running/complete) and when images become available. It supplies none of that, leaving the agent to guess whether calling it repeatedly or downloading early is safe.

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?

Schema description coverage is 0%, so the description must compensate. It explains download partially (saves images locally, default false in schema) but omits the destination path, and job_id is left entirely undocumented, though its meaning is self-evident from the name.

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?

States a specific verb+resource: retrieving the status of a job. Clear enough to distinguish it from cancel_job and generate_image, though it never names the siblings it is contrasted with.

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?

No guidance on when to call this (e.g., polling after generate_image/run_workflow, or checking before fetching results) and no exclusions. The only usage hint is the conditional 'with download=True', which is really parameter behavior rather than selection guidance.

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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