Skip to main content
Glama
Sign In
Kiln

Kiln

by codeofaxel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

get_feedback_loop_status

Read-only

Retrieve iteration history and resolution status for a generated 3D model to identify which iteration produced the best result.

Instructions

Get the feedback loop history for a generated model.

Returns iteration data, whether the design was resolved, and
which iteration produced the best result.

Args:
    model_id: Model/job ID from a generation job.

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": "get_feedback_loop_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_idYesModel/job ID from a generation job.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.5.1
    • addedInput schema / properties / model_id / description
      Added value: +"Model/job ID from a generation job."
  2. First observedv0.1.0

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description still adds real value by enumerating what comes back: iteration data, a resolved/unresolved flag and the best-performing iteration. It omits pagination or size behaviour for long histories, so it stops short of a 5.

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?

The first two sentences are front-loaded and earn their place by establishing scope and return contents. The Args section merely duplicates the schema's 100%-covered parameter description, which is mild padding in an otherwise tight definition.

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?

With no output schema, the description does a reasonable job of sketching the return shape, and annotations cover mutation risk. What is missing is disambiguation from the near-identically named generation_feedback_loop_status and analyse-style sibling, which an agent needs in order to pick the right tool.

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 single parameter is fully documented there; the Args block repeats the schema text verbatim without adding format hints, validity constraints or example values. Baseline 3 applies when the schema does all the work.

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 and resource: 'Get the feedback loop history for a generated model', scoped to generated models. It does not, however, distinguish itself from the closely named sibling generation_feedback_loop_status or from analyze_generation_feedback, both of which sound like they cover similar ground.

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?

The description says nothing about when to call this instead of the similarly named siblings (generation_feedback_loop_status, analyze_generation_feedback) or about prerequisites such as the job having finished. The only contextual hint is the phrase 'for a generated model'.

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

Deploy Server

Other Tools