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plex_rag

Perform natural language semantic search across your Plex library, including metadata and subtitles. Sync and enrich metadata to power deep media discovery.

Instructions

RAG integration for Plex Media. Semantic search and metadata enrichment.

PORTMANTEAU PATTERN RATIONALE: Consolidates neural search, metadata vectorization, and external high-value discovery into a single tool to manage the knowledge lifecycle.

OPERATIONS:

  • semantic_search: Natural language search across indexed Plex metadata (Title, Plot, etc.).

  • search_subtitles: Semantic search across indexed dialogue/subtitles for deep content discovery.

  • sync_metadata: Extract and vectorize core metadata into the local store.

  • sync_subtitles: Download, parse, and index subtitle tracks for semantic dialogue search.

  • status: Check the health and document counts of RAG indices.

Return Format

{"success": bool, "operation": str, "data": dict | list, "count": int | None, "error": str | None}

Examples

await plex_rag(operation="semantic_search", query="time travel paradox") await plex_rag(operation="sync_metadata", enrich=True) await plex_rag(operation="status")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of search results to return.
queryNoSearch query for semantic search operations.
enrichNoWhether to enrich metadata with Wikipedia summaries.
media_idNoSpecific media ID to target for sync operations.
operationYesThe RAG operation to perform.
library_idNoSpecific library ID to target for sync operations.
Behavior3/5

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

Annotations only specify readOnlyHint=false and destructiveHint=false, which is minimal. The description adds useful safety-relevant context: sync operations download and index content (implying mutation), and status checks health. However, it doesn't disclose potential side effects like API rate limits, storage growth, or how sync handles existing data. Since annotations are sparse, the description partially compensates but leaves gaps.

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 well-structured with headers, bullet points, and examples. It front-loads the core purpose and operations, then adds details. The portmanteau rationale paragraph is somewhat verbose and could be trimmed, but the overall structure is efficient and readable.

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 no output schema, the description provides a return format and examples, which is helpful. It covers all operations and key parameters. However, given the complexity of a multi-operation RAG tool, it could elaborate on error handling or index lifecycle (e.g., when to sync vs search), but the provided examples and return format make it sufficient for basic invocation.

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

Parameters3/5

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

Schema description coverage is 100%, so the input schema already documents each parameter. The description adds contextual grouping (e.g., 'query for search operations', 'media_id/library_id for sync operations') but doesn't go beyond the schema's own descriptions. The examples clarify parameter usage but are redundant with schema defaults, so score stays at baseline 3.

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 clearly identifies this as a RAG integration tool for Plex Media with a list of operations (semantic_search, search_subtitles, sync_metadata, sync_subtitles, status). It distinguishes itself from siblings by focusing on semantic search and knowledge lifecycle, though it doesn't explicitly contrast with specific siblings like plex_search.

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

Usage Guidelines3/5

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

The description implies usage through the operation examples, but it doesn't provide explicit when-to-use guidance or mention alternatives. The portmanteau rationale explains why operations are consolidated but lacks concrete situational context (e.g., 'use semantic_search when you need natural language queries rather than keyword search').

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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