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SavageCore

lidarr-mcp

by SavageCore

lidarr_notifications_metadata

Manage Lidarr notifications and metadata consumers: list, create, update, test, or delete them using the specified operation and arguments.

Instructions

lidarr notifications metadata operations on Lidarr. Pass operation and an arguments dict matching that operation's parameters.

  • lidarr_action_metadata(name, body={}) — Run a metadata consumer provider-specific action. WRITE: this modifies your Lidarr instance.

  • lidarr_action_notification(name, body={}) — Run a notification provider-specific action. WRITE: this modifies your Lidarr instance.

  • lidarr_create_metadata(force_save=False, body={}) — Add a metadata consumer (body is a MetadataResource). WRITE: this modifies your Lidarr instance.

  • lidarr_create_notification(force_save=False, body={}) — Add a notification (body is a NotificationResource). WRITE: this modifies your Lidarr instance.

  • lidarr_delete_metadata(id) — Delete a metadata consumer. DESTRUCTIVE: this deletes data.

  • lidarr_delete_notification(id) — Delete a notification. DESTRUCTIVE: this deletes data.

  • lidarr_get_metadata(id) — Fetch a single metadata consumer by id.

  • lidarr_get_metadata_schema() — Return the metadata consumer schema (available implementations).

  • lidarr_get_notification(id) — Fetch a single notification by id.

  • lidarr_get_notification_schema() — Return the notification schema (available implementations).

  • lidarr_list_metadata() — List metadata consumers (Kodi, Plex, Emby NFO writers).

  • lidarr_list_notification() — List notifications.

  • lidarr_test_all_metadata() — Test all metadata consumers. WRITE: this modifies your Lidarr instance.

  • lidarr_test_all_notification() — Test all notifications. WRITE: this modifies your Lidarr instance.

  • lidarr_test_metadata(force_test=False, body={}) — Test a metadata consumer configuration (body, without saving). WRITE: this modifies your Lidarr instance.

  • lidarr_test_notification(force_test=False, body={}) — Test a notification configuration (body, without saving). WRITE: this modifies your Lidarr instance.

  • lidarr_update_metadata(id, force_save=False, body={}) — Update a metadata consumer. WRITE: this modifies your Lidarr instance.

  • lidarr_update_notification(id, force_save=False, body={}) — Update a notification. WRITE: this modifies your Lidarr instance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argumentsNo
operationYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations available, the description carries the burden of disclosing side effects. It explicitly labels write operations and destructive deletions, and clarifies that test operations do not save configurations. It does not mention auth requirements or rate limits, but the safety profile is reasonably covered.

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 opens with a concise dispatch instruction, then presents a structured bullet list that groups all 18 operations. Each line earns its place by providing the operation name and a brief, non-redundant explanation. The formatting is very readable despite the length.

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?

Given the tool's complexity (18 operations), the absence of annotations, and an input schema that is nearly empty, the description covers every operation's purpose, marks write/destructive behaviors, and gives parameter hints. An output schema exists, so return values don't need detailed explanation. The tool is fully comprehensible from the description alone.

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?

The input schema only defines a generic operation enum and a free-form arguments object. The description adds significant meaning by showing function-style signatures for each operation (e.g., 'id', 'force_save', 'body') and noting that body is a MetadataResource or NotificationResource. This compensates for the schema's 0% description coverage, though type details remain high-level.

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 begins with a clear statement that this tool handles Lidarr notifications and metadata operations, then enumerates every sub-operation with a specific verb and target (e.g., 'lidarr_create_metadata', 'lidarr_list_notification'). This distinguishes it from sibling tools that cover other domains such as release search or media library.

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 clearly delineates the scope of the tool (notifications and metadata consumers) and lists each operation, making it obvious when to use this tool. However, it does not explicitly name sibling tools or provide exclusionary guidance, so it stops short of the top score.

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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