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ktalk_get_transcript

Retrieve the transcript of a KTalk recording with speaker labels. Choose between raw JSON or markdown format with timecodes, and split large transcripts into manageable chunks.

Instructions

Get transcript of a KTalk recording (speech-to-text by speakers).

Args: recording_key: Recording key/identifier (required) format: Output format — "raw" (JSON) or "markdown" (dialogue with timecodes) chunk: Chunk number. 0 = auto (returns full text if small, first chunk if large). 1+ = specific chunk number for paged reading. chunk_size: Max characters per chunk (~7500 tokens at 30000). Soft limit — chunks split at utterance boundaries, never mid-utterance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chunkNo
formatNomarkdown
chunk_sizeNo
recording_keyYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries full behavioral burden. It details chunking behavior (auto vs specific chunk, utterance boundaries) and format options. It does not mention safety or error handling, but the output schema covers return value expectations.

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 structured as a docstring with parameter list. It is informative but slightly verbose. All sentences add value, though some could be tightened.

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?

With no annotations, the description covers key behavioral aspects (chunking, format). The output schema reduces need to describe return values. However, it lacks details on error states or prerequisite recording existence.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description thoroughly explains each parameter: recording_key (required), format (raw vs markdown), chunk (auto mode and paging), and chunk_size (soft limit with utterance boundary splitting). This adds significant meaning beyond 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 clearly states 'Get transcript of a KTalk recording (speech-to-text by speakers)', providing a specific verb and resource. It distinguishes from sibling tools like ktalk_get_summary and ktalk_list_recordings by focusing on transcript retrieval.

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 does not explicitly state when to use this tool versus alternatives. However, the sibling tool names (e.g., summary, list) imply distinct use cases. No when-not-to-use or prerequisite guidance is provided.

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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