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get_segments_text

Retrieve plain text from all segments of an asset to extract transcriptions, captions, or spoken content for review and analysis.

Instructions

Get all segment text content as plain text

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asset_idYesThe asset UUID

Schema Changelog

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

  1. First observedv1.0.0

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 burden of behavioral disclosure. It does state that the tool returns all segment text content in plain text, which is useful, but it gives no information about ordering, concatenation behavior, how empty results are handled, whether timestamps are included, or any side effects/authorization requirements. For a tool with zero annotation support, this 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear, front-loaded sentence with no filler. It efficiently communicates the core behavior without redundancy. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter read tool, the description mostly covers what an agent needs: the resource (segments), the operation (get all), and the output format (plain text). However, without an output schema, the description could more explicitly state whether the return value is a single string, a list, or a structured object. It also does not clarify whether 'segments' refers to transcript segments, which would be helpful given siblings like search_segments and list_asset_segments.

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?

The input schema fully documents the only parameter, asset_id, with a format and description ('The asset UUID'). Schema description coverage is 100%, so the baseline is 3. The tool description adds no additional meaning about how asset_id influences the result, but it does not need to because the schema already covers the parameter adequately.

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 states a specific action ('Get all segment text content') and the output format ('as plain text'), making it clear that this tool returns the combined text of all segments. It is clearly distinguishable from export_segments_srt and export_segments_vtt because those produce subtitle formats while this returns plain text. However, it does not explicitly contrast itself with sibling tools such as list_asset_segments or search_segments, so a fully differentiated agent might still need to infer the distinction.

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?

There is no guidance about when to choose this tool over alternatives such as export_segments_srt, export_segments_vtt, list_asset_segments, or get_asset_segment. The description communicates what the tool does but not the situations in which it is preferred, nor when it should be avoided. An agent is left to infer usage context entirely from the tool name and sibling list.

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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