Skip to main content
Glama
ni-c

audiobookshelf-mcp

by ni-c

Get podcast episode

get_podcast_episode
Read-only

Retrieve a podcast episode's publication date, duration, and description by supplying its podcast and episode IDs. Choose compact or full detail to control response size.

Instructions

Fetches one podcast episode with its publication date, duration and description.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailNo"compact" (default) returns a projection with the fields that matter for browsing; "full" returns the raw Audiobookshelf object including audio files, tracks and chapters, which is very large.
episode_idYesPodcast episode id
library_item_idYesLibrary item id of the podcast the episode belongs to

Schema Changelog

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

  1. First observedv0.1.1

TDQS

B3.3/5.0
Behavior3/5

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

The description aligns with the readOnlyHint annotation by stating 'Fetches'. However, it does not disclose behavioral traits beyond the annotation, such as the impact of the detail parameter on response size or latency. The description adds minimal value over the structured annotation.

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 a single sentence of 13 words, highly concise and front-loaded. It could be slightly more informative (e.g., mentioning the detail parameter), but it remains efficient and avoids redundancy.

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?

Without an output schema, the description should give a fuller picture of the response. It only lists three fields (publication date, duration, description), but the detail parameter implies a much larger response for 'full' mode. This omission leaves the agent under-informed about the complete return shape.

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 schema already documents all three parameters (library_item_id, episode_id, detail). The description adds no additional meaning about the parameters; it only mentions response fields. Baseline score of 3 is appropriate.

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 clearly states the verb 'Fetches' and the resource 'one podcast episode', listing specific fields (publication date, duration, description). This makes the tool's purpose unambiguous and distinct from sibling tools like list_recent_episodes (list) or get_library_item (generic item).

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?

The description provides no guidance on when to use this tool over alternatives, such as list_recent_episodes or get_library_item. It also does not mention the trade-off between the 'compact' and 'full' detail parameter, leaving the agent to infer usage alone.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ni-c/audiobookshelf-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server