FARPY

status

Fetch the normalized state of a Blender rendering job and polling recommendations using the job ID. Provides clear guidance on when to check again, without worker details.

Instructions

Read normalized job state and polling guidance. No worker, provider, or progress-percent fields.

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": "status"
  }
}

Input Schema

Name	Required	Description	Default
`job_id`	Yes	FARPY job id.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 3, 2026

TDQS

A3.9/5.0

Behavior4/5

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

No annotations are provided, so the description carries the transparency burden. It does disclose that the result is normalized and explicitly warns that worker, provider, and progress-percent fields are absent, which prevents false expectations. It does not cover error behavior or polling guidance format, but this is still meaningful disclosure for a simple read 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?

The description is two short clauses with no filler. Every phrase contributes either to what the tool does, what it returns guidance for, or what it omits, making it appropriately concise and front-loaded.

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

Completeness3/5

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

The tool is simple with one parameter, but there is no output schema, so the description should clarify the state vocabulary or polling guidance shape. It explains the high-level purpose and exclusions but leaves the actual return content vague, which is a real gap for an agent trying to interpret the result.

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 100% for the single job_id parameter, so the schema already documents the parameter fully. The description adds no additional meaning about the parameter format or semantics beyond what the schema provides.

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 states a specific operation ('Read normalized job state') and a clear deliverable ('polling guidance'), and it narrows scope by explicitly listing excluded fields. The negative field list helps distinguish this tool from sibling read-style tools even without naming them.

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?

The description implies the tool is used for reading job status and polling, but it never explicitly says when to use status over inspect, quote, or receipt. There are no stated alternatives or exclusions, leaving the routing decision mostly to inference.

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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