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get_speaker_turns

Retrieve speaker turns from an AssemblyAI transcript that has speaker labels. Get who said what, with optional limit on number of turns.

Instructions

Get who said what, for a transcript made with speaker_labels.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_turnsNoHow many turns to return. Default 100
transcript_idYesId of a transcript made with speaker_labels

Schema Changelog

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

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description carries the full transparency burden. It discloses the speaker_labels prerequisite and the general return concept, but says nothing about ordering, pagination, errors for unlabeled transcripts, or how max_turns affects results. This is a significant gap for a data-retrieval tool.

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 sentence that front-loads the core purpose with no filler. It is appropriately succinct, though slightly too terse to carry all necessary behavioral context on its own.

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 tool with no annotations, no output schema, and siblings like get_transcript and search_transcript, this description is incomplete. It covers the speaker_labels prerequisite but not the return shape, failure behavior, or relationship to sibling tools, leaving an agent to guess important call-time details.

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 both parameters are already documented. The description adds no new parameter-level meaning beyond restating the speaker_labels condition already present in the transcript_id schema, so the baseline 3 applies.

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 clearly identifies the action ('Get') and the resource ('who said what' / speaker turns), and adds the relevant condition of a transcript made with speaker_labels. It does not explicitly distinguish itself from get_transcript or search_transcript, so it stops short of a 5.

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?

It communicates when the tool is appropriate: for transcripts made with speaker_labels. However, it provides no guidance on when not to use it or which sibling alternative to choose, such as get_transcript for transcripts without speaker labels.

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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