3DLogo.io

Check a Generate run

get_generation_status

Read-onlyIdempotent

Status of a run started by generate_3d_model (or from the user's history). When completed, save it with save_generation_as_project.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`run_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description
`error`	Yes
`runId`	Yes
`status`	Yes
`openUrl`	Yes	The run in the Generate studio
`refunded`	Yes
`modelName`	Yes
`hasPreview`	Yes	True once a render of the mesh exists; get_generation_preview returns it
`queuePosition`	Yes
`creditsCharged`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed6 schema fields changedSep 6, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

changedOutput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

removedOutput schema / properties / creditsCharged / anyOf

Removed value: -[
-  {
-    "type": "number"
-  },
-  {
-    "type": "null"
-  }
-]

addedOutput schema / properties / creditsCharged / type
```
Added value: +[
+  "number",
+  "null"
+]
```

removedOutput schema / properties / error / anyOf

Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]

addedOutput schema / properties / error / type
```
Added value: +[
+  "string",
+  "null"
+]
```

First observedSep 6, 2026

TDQS

A4.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds workflow context (which run it applies to and what to do when complete), but does not describe behavioral details such as polling semantics or error on unknown run_id; with annotations, this is adequate.

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?

Two short sentences, front-loaded with the tool's purpose and immediately followed by the workflow note. No filler or repetition of schema or annotation details.

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

Completeness5/5

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

For a one-parameter status checker with a readOnly/idempotent annotation and an output schema, the description covers what the run is, where the ID comes from, and what to do after completion. Nothing essential is missing for correct invocation.

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

Parameters4/5

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

Schema coverage is 0% for the single run_id parameter, so the description must compensate. It does so by explaining that the run is one started by generate_3d_model or found in user history, giving semantic meaning to run_id beyond its UUID type. It does not explicitly spell out that run_id is required, but the schema marks it required.

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 identifies the resource ('a run'), the process that creates it ('generate_3d_model'), and the title supplies the verb ('Check'). It is distinct from list_generations, which lists runs, and from save_generation_as_project, which persists a completed run, so an agent can route to it correctly.

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

Usage Guidelines4/5

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

It gives a clear context for use: status of a run from generate_3d_model or user history, and it tells the agent the follow-up action once completed ('save it with save_generation_as_project'). It does not explicitly name alternatives like list_generations for finding history runs, so it earns a 4 rather than a 5.

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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TDQS

A3.8/5.0

Disambiguation3/5

Most tools target distinct resources, but several overlapping pairs exist: search/list_projects both find projects by title, fetch/get_project both return project details, and upload_logo_image/request_logo_image_upload are two upload paths. The descriptions help clarify boundaries, but an agent could still misselect.

Naming Consistency4/5

Tool names overwhelmingly follow a clear verb_noun snake_case pattern (create_, list_, get_, update_, delete_). Minor deviations like bare 'fetch' and 'search', plus the mixed '3d' in generate_3d_model vs '3D' in descriptions, keep it from being perfectly consistent.

Tool Count2/5

At 31 tools, this exceeds the 25+ threshold where agent tool selection becomes cognitively heavy. While the server covers a broad platform, several tools are near-redundant and could be consolidated, making the count feel inflated.

Completeness4/5

The surface covers project lifecycle, sharing/publishing, AI generation, uploads, materials, and account/plan management quite thoroughly. Minor gaps exist, such as no direct create_coin_project tool and no deletion for generation runs, but these are workable.

Resources

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