@withpica/mcp-server/dist/tools/audio-files.d.ts

MCP Server for withPICA — enables AI assistants to interact with your withPICA music catalog

/**
 * Audio Files Tools - MCP tools for managing audio files and analysis
 */
import { PicaClient } from "@withpica/mcp-sdk";
import { ToolDefinition, ToolExecutor } from "./index.js";
/**
 * Project one audio row to its list shape. Dropped on purpose:
 * `waveform_data` (the weight — on pica_audio_inspect sections:["waveform"]),
 * sample-rate / bit-depth / danceability (inspect sections:["analysis"]),
 * and `metadata`, `file_path`, `cdn_url`, `extraction_error`, analysis
 * version/processor and the `recording` / `work` embeds — storage internals
 * not exposed over MCP. See queryAudioFiles for the measurement.
 */
export declare function toAudioListItem(row: unknown): Record<string, unknown>;
/**
 * The `identification` section of pica_audio_inspect — what ACRCloud said this
 * file is. Built from the single-file GET, which already carries the
 * audio_analysis_results satellite flattened onto the row (findById in
 * lib/services/audio-files/queries.ts selects `audio_analysis_results(*)`), so
 * the section costs no extra call.
 *
 * Before 2026-09-26 no read tool exposed these columns: the only way an agent
 * saw a match was the full row echoed by pica_audio_update, which #2360
 * slimmed — leaving them unreachable (ops issue 3cf19a67).
 *
 * Deliberately lean: each DSP entry keeps only track_id + link (artwork,
 * preview URLs and artist genres are dropped), and the ACRCloud AI-detection
 * payload is cut to its headline fields (the per-stem and per-generator
 * probability arrays are left out). Values are copied as stored, never
 * rescaled: `identification_confidence` is ACRCloud's match score as written
 * (25-100 across prod rows on 2026-09-26). `ai_detection.confidence` is the
 * AI probability on 0-100 (the AiDetectionAcrcloud type). Its prod values
 * span 0.2-96.47, which looked like mixed scales, but read against the
 * verdict on 2026-09-26 (723 rows, one model_id): every `ai_generated` row is
 * ≥ 61.91 and every `human` row ≤ 47.71, and all 363 values ≤ 1 are `human`
 * — one 0-100 scale, with human tracks scoring near zero. A file never run
 * through ACRCloud reports `identification_status: null`.
 */
export declare function toIdentificationSection(file: unknown): Record<string, unknown>;
/**
 * The one-line summary of a matched pica_audio_identify.
 *
 * The route (app/api/admin/audio-files/[id]/identify) returns the service's
 * IdentificationResult FLAT — `title`, `artist`, `confidence`,
 * `externalIds.isrc[]` at the top level. Until 2026-09-26 this tool read a
 * `result.match` object the route has never sent, so a real match never
 * reached this branch; and it multiplied `confidence` by 100, which only a
 * 0-1 score would need. ACRCloud's music score is 0-100 — every stored
 * `audio_analysis_results.identification_confidence` on prod reads 25-100
 * (2026-09-26) — so it is printed as-is.
 */
export declare function describeIdentifyMatch(result: unknown): string;
export declare class AudioFilesTools {
    private pica;
    constructor(pica: PicaClient);
    getTools(): Array<{
        definition: ToolDefinition;
        executor: ToolExecutor;
    }>;
    private assignAudio;
    /**
     * When an assign failed and one of the passed ids exists as the OTHER
     * entity type, name the mistake precisely. Returns null when the probe
     * finds nothing (the original error is rethrown by the caller).
     */
    private wrongIdTypeHint;
    private bulkAssignSuggested;
    private dismissMatch;
    private updateAudio;
    private bulkUpdateAudio;
    private deleteAudioFile;
    private queryAudioFiles;
    private inspectAudioFile;
    private analyzeAudioFile;
    private presignedUpload;
    private completeUpload;
    private identifyAudioFile;
    /**
     * ADR-232 § Decision 4 — pica_audio_pipeline_status executor.
     * Forwards to the SDK; the service-layer aggregator does the work.
     */
    private audioPipelineStatus;
}
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