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jellyfin_task_run

Start a Jellyfin scheduled task by name or key, like scanning media or cleaning cache. Dry-run first, then apply with confirmation.

Instructions

Start a Jellyfin scheduled task by name or key (e.g. "Scan Media Library", "Extract Chapter Images", "Clean Cache", RefreshLibrary).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hostYeshost name from the inventory that has apps.jellyfin / apps.tdarr
taskYes
applyNofalse (default) = dry run: show what will happen on which hosts and a planHash. true = run it; needs planHash
planHashNoplanHash from the dry run of the very same call, good for one apply; refused if targets or their state changed since

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden and says nothing about side effects. It never mentions that this is a mutating action, that the default is a dry run requiring a planHash to actually execute, whether the call waits for task completion, or what happens if the task name/key is invalid.

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?

A single front-loaded sentence with no filler; the examples are compact and useful. It is arguably under-sized rather than bloated, but as written every element earns its place.

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

Completeness2/5

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

For a mutation tool with no annotations and no output schema, the description omits the two-phase dry-run/apply requirement, expected result, and error behavior. The agent must reconstruct the safety protocol from the schema, which is insufficient for correct 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 coverage is 75%: host, apply, and planHash are documented in the schema, but the required 'task' parameter has no schema description. The description compensates by supplying example task names/keys for 'task', adding real value, though it does not clarify valid key formats or name matching rules.

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 gives a specific verb+resource ('Start a Jellyfin scheduled task') and clarifies that lookup can be by name or key, with concrete task examples ("Scan Media Library", "Clean Cache", RefreshLibrary). It does not distinguish this from the sibling jellyfin_scan, however, which may cover overlapping ground (both trigger media scans).

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

Usage Guidelines2/5

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

There is no statement of when to use this tool versus jellyfin_scan or the other jellyfin_* siblings, nor any prerequisites. The only procedural guidance (dry-run first, then apply) lives in the schema parameter descriptions, not the tool description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.