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gaudio_create_job

Create a processing job for uploaded audio files using AI models like stem separation or DME. For stem separation, specify the desired stem types.

Instructions

Create a processing job with an uploaded file. For Stem Separation models (gsep_music_hq_v1, gsep_music_shq_v1, gsep_speech_hq_v1), the 'type' parameter is required (e.g. 'vocal', 'vocal,drum'). For DME models, no type is needed. For Text Sync (gts_lyrics_line_v1), use gaudio_sync_lyrics instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uploadIdYesUpload ID from gaudio_upload_file (used as audioUploadId)
modelYesModel name (e.g. gsep_music_hq_v1, gsep_dme_dtrack_v1)
typeNoStem type(s), comma-separated. Required for Stem Separation models. e.g. 'vocal', 'vocal,drum,bass'

Implementation Reference

  • The handler function that registers and implements the 'gaudio_create_job' tool. It defines a Zod schema with uploadId, model, and optional type parameters; validates the model exists and type is provided when required; calls client.createJob(model, params) to create the job; and returns the jobId, model, and status.
    import { z } from "zod";
    import type { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
    import type { GaudioClient } from "../api/client.js";
    import { getModel } from "../models/registry.js";
    
    export function registerCreateJob(server: McpServer, client: GaudioClient) {
      server.tool(
        "gaudio_create_job",
        "Create a processing job with an uploaded file. For Stem Separation models (gsep_music_hq_v1, gsep_music_shq_v1, gsep_speech_hq_v1), the 'type' parameter is required (e.g. 'vocal', 'vocal,drum'). For DME models, no type is needed. For Text Sync (gts_lyrics_line_v1), use gaudio_sync_lyrics instead.",
        {
          uploadId: z.string().describe("Upload ID from gaudio_upload_file (used as audioUploadId)"),
          model: z.string().describe("Model name (e.g. gsep_music_hq_v1, gsep_dme_dtrack_v1)"),
          type: z
            .string()
            .optional()
            .describe("Stem type(s), comma-separated. Required for Stem Separation models. e.g. 'vocal', 'vocal,drum,bass'"),
        },
        async ({ uploadId, model, type }) => {
          const modelInfo = getModel(model);
          if (!modelInfo) {
            return {
              content: [{ type: "text" as const, text: `Unknown model: ${model}. Use gaudio_list_models to see available models.` }],
              isError: true,
            };
          }
    
          if (modelInfo.typeRequired && !type) {
            return {
              content: [
                {
                  type: "text" as const,
                  text: `Model ${model} requires a 'type' parameter. Options: ${modelInfo.typeOptions?.join(", ")}`,
                },
              ],
              isError: true,
            };
          }
    
          const params: Record<string, unknown> = { audioUploadId: uploadId };
          if (type) params.type = type;
    
          const { jobId } = await client.createJob(model, params);
    
          return {
            content: [
              {
                type: "text" as const,
                text: JSON.stringify({ jobId, model, status: "created" }, null, 2),
              },
            ],
          };
        },
      );
    }
  • src/index.ts:29-37 (registration)
    Registration of the gaudio_create_job tool by calling registerCreateJob(server, client) in the main entry point.
    registerCreateJob(server, client);
    registerGetJob(server, client);
    registerSeparateAudio(server, client);
    registerSyncLyrics(server, client);
    registerGetKeyInfo(server, client);
    
    const transport = new StdioServerTransport();
    await server.connect(transport);
  • Zod schema for gaudio_create_job: uploadId (string), model (string), type (optional string for stem separation).
    {
      uploadId: z.string().describe("Upload ID from gaudio_upload_file (used as audioUploadId)"),
      model: z.string().describe("Model name (e.g. gsep_music_hq_v1, gsep_dme_dtrack_v1)"),
      type: z
        .string()
        .optional()
        .describe("Stem type(s), comma-separated. Required for Stem Separation models. e.g. 'vocal', 'vocal,drum,bass'"),
    },
  • The client.createJob method called by the handler. It POSTs to /{model}/jobs with the provided params (including audioUploadId and optional type) and returns the jobId from the response.
    async createJob(
      model: string,
      params: Record<string, unknown>,
    ): Promise<{ jobId: string }> {
      const res = await this.request("POST", `/${model}/jobs`, params);
      return { jobId: res.resultData?.jobId as string };
    }
  • The getModel helper used by the handler to validate the model name exists in the registry.
    export function getModel(name: string): ModelInfo | undefined {
      return MODEL_REGISTRY.find((m) => m.name === name);
    }
Behavior4/5

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

Given no annotations, the description discloses key behavioral traits: the conditional requirement of the 'type' parameter based on model selection. It does not mention potential side effects, authentication needs, or error cases, but the provided information is sufficient for safe usage.

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 concise with three sentences, each serving a purpose: stating the action, explaining parameter rules for different models, and providing an alternative tool. No extraneous information.

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

Completeness4/5

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

With three parameters, no output schema, and no annotations, the description adequately explains parameter usage and conditional logic. It could optionally mention the output (e.g., job ID), but the current information is sufficient for the agent to invoke the tool 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%, so baseline is 3. The description adds value by explaining the conditional requirement of the 'type' parameter and providing examples (e.g., 'vocal', 'vocal,drum,bass'), which go beyond the schema's description.

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 'Create a processing job with an uploaded file', identifying the specific verb and resource. It distinguishes from sibling tools by mentioning that for Text Sync, one should use gaudio_sync_lyrics instead.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool: for Stem Separation models, the 'type' parameter is required; for DME models, no type is needed; and for Text Sync, a different tool is recommended. This effectively tells the agent when to use and when not to use the tool.

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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