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gptproto-ai

GPTProto MCP

Official
by gptproto-ai

Create a GPTProto custom generation task

gptproto_custom_create

Submit a custom GPTProto generation request for image, video, speech, voice-clone, lip-sync, 3D, or image-edit using the documented native body. Specify resource and payload to trigger the job.

Instructions

Submit an existing GPTProto custom image, video, audio, lip-sync, 3D, or image-edit request using its documented native body.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYes
labelNoOptional local recovery label; stored locally and never sent to GPTProto
resourceYes
output_jsonNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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

Beyond the annotations (readOnlyHint=false, etc.), the description adds little behavioral context. It does not mention side effects, asynchronous behavior, return format, authentication, or failure modes, which is significant for a write operation with no output schema.

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 description is a single sentence with no filler or repetition. It could be slightly clearer, but it is appropriately compact and reads naturally.

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?

This is a low-level create tool with a free-form nested body, no output schema, and minimal annotations. The description does not explain how to construct the body, what the response will be, or how this differs from high-level endpoints, leaving an agent under-equipped to call it correctly.

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

Parameters2/5

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

With only 25% schema description coverage, the description needed to compensate, but it only hints at the resource modalities and mentions a 'native body' without explaining body, label, output_json, or the full resource enum. It omits 'speech' and 'voice-clone' from the enum, adding potential confusion.

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 ('Submit') and resource ('custom image, video, audio, lip-sync, 3D, or image-edit request'), which makes the core function reasonably clear. However, it does not differentiate this tool from the sibling gptproto_request, and the phrase 'existing ... request' is slightly ambiguous.

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 is given about when to use this tool versus siblings like gptproto_request. The description does not state any prerequisites, exclusions, or context that would help an agent choose this tool over alternatives.

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