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OrellBuehler

Tdarr MCP Server

by OrellBuehler

get_pie_stats

Get pie-chart stats from the Tdarr dashboard: file counts by transcode status, health, container, codec, resolution, and channel layout. Omit library ID for all-library totals.

Instructions

Get the pie-chart statistics shown on the Tdarr dashboard (POST /api/v2/stats/get-pies) — file counts by transcode status, health status, container, video and audio codec, resolution and channel layout. Omit library_id for totals across every library.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
library_idNoLibrary ID to scope the stats to (default: all libraries)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It communicates the read-only nature via 'Get', adds the underlying POST endpoint, specifies what dimensions are aggregated, and clarifies that omitting library_id returns totals across every library. This is strong behavioral context for a low-complexity stats tool.

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?

A single, information-dense sentence front-loads the purpose, adds the API endpoint, enumerates the statistic categories, and closes with the parameter behavior. There is no filler or redundancy.

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?

For a read-only tool with one optional parameter and no output schema, the description is complete: it states what is returned, the scoping parameter's effect, and the endpoint. An agent has everything needed to select and invoke it 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?

The input schema already describes library_id as the scoping ID with a default of all libraries, so baseline is 3. The description adds extra meaning by explaining that omitting it yields 'totals across every library', which clarifies the aggregation semantics beyond a literal reading of the schema.

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 names a specific verb ('Get'), a concrete resource ('pie-chart statistics'), and enumerates exactly what the statistics cover: file counts by transcode status, health status, container, codecs, resolution, and channel layout. This clearly distinguishes it from the many other get_* sibling tools.

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 gives clear context for when the tool is relevant (Tdarr dashboard pie stats) and even explains the default behavior of omitting library_id. However, it does not explicitly say when to choose this tool over related sibling tools like get_resource_stats, get_performance_stats, or get_stream_stats, nor does it state any exclusions.

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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