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Kiln

Kiln

by codeofaxel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

generation_feedback_loop_status

Read-only

Check the status of a 3D model generation feedback loop by model ID to see if it is running, complete, or needs attention.

Instructions

Check the status of a generation feedback loop.

        Args:
            model_id: The model ID of the feedback loop.

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": {
      "model_id": "<model_id>"
    },
    "name": "generation_feedback_loop_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_idYesThe model ID of the feedback loop.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.5.1
    • addedInput schema / properties / model_id / description
      Added value: +"The model ID of the feedback loop."
  2. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description adds little beyond that: it does not describe what status values are returned, whether it should be polled, or what happens if the model_id is unknown. With annotations carrying the main behavioral burden, a 3 is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening sentence is front-loaded and clear, but the docstring-style Args block duplicates the input schema without adding value. It is short, but not every part earns its place.

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?

For a simple one-parameter status tool, the description is minimally adequate. However, with no output schema and no explanation of return values or polling behavior, the agent lacks enough context to interpret the result beyond the bare operation.

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%, and the description merely repeats the schema's model_id description verbatim. It adds no meaning about format, provenance, or how to obtain the model ID. Baseline 3 applies because the schema fully documents the only parameter.

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 states a specific verb (check) and resource (status of a generation feedback loop), so the agent knows the basic operation. However, it does not distinguish this from the near-duplicate sibling get_feedback_loop_status or from related tools such as generation_status or analyze_generation_feedback.

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?

There is no explicit guidance on when to use this tool versus the many related status and feedback tools in the sibling list. The agent must infer that this is a polling/status-check tool, and nothing indicates prerequisites or alternatives.

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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