Retro Diffusion Pixel Art MCP

Get Generated Outputs

get_inference_result

Read-onlyIdempotent

Retrieve retained outputs for a synchronous generation.

Use the request_id returned by create_inference to recover or refresh signed output URLs. This lookup is read-only; never repeat a paid create_inference call just because delivery URLs are temporarily missing.

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": {
      "request_id": "<request_id>"
    },
    "name": "get_inference_result"
  }
}

Input Schema

Name	Required	Description	Default
`rd_api_key`	No	RetroDiffusion API key (rdpk-...) for this call only; overrides session or header auth.
`request_id`	Yes	Inference request id returned by create_inference.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 2, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description reinforces this with 'This lookup is read-only.' It adds valuable context beyond annotations: the tool returns signed output URLs and warns against unnecessary paid calls, which is a meaningful economic/behavioral nuance. No contradiction.

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 three short, purposeful paragraphs: purpose, usage, and warning. Every sentence earns its place, with no filler. The front-loaded purpose makes it immediately clear what the tool does.

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?

This is a simple lookup tool with an output schema and strong annotations. The description covers the synchronous generation context, how to obtain the request_id, and the read-only nature. There are no significant gaps for an agent to invoke it correctly.

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 100% (both request_id and rd_api_key are documented). The description adds contextual meaning by linking request_id to create_inference's return value, clarifying the parameter's origin. This goes beyond the schema description, justifying a score above the baseline 3.

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 clearly states the tool's purpose: 'Retrieve retained outputs for a synchronous generation.' It uses a specific verb ('retrieve') and resource ('retained outputs'), and distinguishes itself from siblings like get_inference_job and create_inference by focusing on output retrieval via request_id.

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?

The description provides explicit usage context: use the request_id from create_inference to recover/refresh signed URLs. It also cautions against repeating a paid create_inference call, which is a clear anti-pattern. It doesn't explicitly contrast with alternative tools like get_inference_job, but the guidance is strong.

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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