Skip to main content
Glama
calebn

Podcast MCP

by calebn
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

transcribe_track

Transcribe one podcast track—or all dialogue tracks when track_id is omitted—into word-level timestamps. Reuses ASR cache when audio, model, and prompt are unchanged.

Instructions

Transcribe one track, or all dialogue tracks when track_id is omitted, into word-level timestamps.

Runs even when a transcript exists, but reuses the ASR disk cache when the audio, model and prompt are unchanged (the pipeline's force_transcribe skips that cache). Replaces only those transcripts (others and extra-source transcripts are kept) and warns when it replaces a hand-edited one; the pipeline's transcribe_tracks step reuses existing transcripts instead.

Input Schema

NameRequiredDescriptionDefault
track_idNo
project_pathNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.1.1
    • addedInput schema / properties / project_path / anyOf
      Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
    • addedInput schema / properties / project_path / default
      Added value: +null
    • removedInput schema / properties / project_path / type
      Removed value: -"string"
    • removedInput schema / required
      Removed value: -[
-  "project_path"
-]
  2. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations present, the description carries the full burden and does so well: it discloses cache-reuse behavior tied to unchanged audio/model/prompt, specifies that only matching transcripts are replaced while others and extra-source transcripts are kept, and warns on overwriting a hand-edited transcript. It omits auth/permission requirements and any note on runtime cost, which for an ASR operation is a meaningful gap.

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 core behavior is front-loaded in the first sentence, followed by dense but relevant replacement/caching detail. The parenthetical contrasting the pipeline's force_transcribe is somewhat tangential and adds density, but nearly every clause carries decision-relevant information.

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?

An output schema exists, so return values need not be explained, and the description covers the mutation semantics, caching, and replacement scope thoroughly. The unexplained project_path parameter is the only notable gap for a two-parameter, high-complexity operation.

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 0% with two parameters. The description fully explains track_id's null/omitted semantics ('all dialogue tracks when track_id is omitted'), which is valuable, but project_path is never mentioned, leaving one of two parameters undocumented in both schema and description.

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?

States a specific verb (Transcribe) and resource (track / all dialogue tracks), plus the exact scope rule: one track, or all dialogue tracks when track_id is omitted. It also declares the output form (word-level timestamps), so an agent knows precisely what the tool produces and how it differs from read-only siblings like get_transcript.

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?

Gives clear context for when to reach for it versus the pipeline path, noting that the pipeline's transcribe_tracks step reuses existing transcripts whereas this tool re-runs, and that force_transcribe skips the cache. It stops short of explicitly naming a non-pipeline sibling to use instead in borderline cases, so it is strong but not fully prescriptive.

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

Deploy Server

Other Tools