Skip to main content
Glama

generate_captions

Generate captions from a video transcript for a specified track, choosing a style among default, bold, minimal, or social.

Instructions

Generate captions from the transcript.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleNo
track_idYes
index_dirNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

D1.8/5.0
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'generate captions' without explaining side effects, return format, or whether it modifies anything. There is no information about what happens to the transcript or how the captions are delivered.

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, concise sentence with no wasted words. The purpose is front-loaded. However, it is under-specified for the tool's complexity, but that's more a completeness issue than conciseness. Structure is efficient.

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

Completeness1/5

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

For a tool with 3 parameters, no output schema, and no annotations, the description is grossly incomplete. It does not explain what the parameters do, what the output looks like, or how to use the tool effectively. An agent would have to guess essential details.

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

Parameters1/5

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

Schema description coverage is 0%, so the schema provides no explanations. The description does not mention any parameters, leaving the agent to infer from names like 'style', 'track_id', and 'index_dir'. It fails to compensate for the missing schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear action ('Generate captions from the transcript') with a verb and resource, but it is vague about what 'transcript' refers to and what the captions are for. It doesn't explicitly distinguish from siblings because no similar caption tool exists, but it could be more specific about the context (e.g., which track's transcript).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided about when to use this tool versus alternatives. There are no prerequisites, conditions, or exclusions mentioned. The description is a single sentence with no usage context.

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