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Glama

get_transcript

Retrieves timestamped transcript segments for a video between start and end times, helping AI agents review spoken content in context.

Instructions

Retrieve timestamped transcript segments for a video within [t_start, t_end].

Input Schema

NameRequiredDescriptionDefault
t_endNo
t_startNo
video_sha256Yes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With zero annotations, the description carries the full behavioral burden but discloses almost nothing: no time units (seconds vs milliseconds), no indication of what happens when the video has no transcript or an unfinished ingest, and no sense of return volume or truncation for long videos. Only the bracketed range convention hints at inclusive-range behavior.

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?

A single tight sentence with the resource front-loaded and the scope qualifier attached. Nothing is padded or redundant, though the brevity is partly the cause of the coverage gaps.

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 3-parameter tool with no annotations, no output schema, and no parameter documentation, the description leaves too much unsaid: units, defaults behavior (the schema defaults t_start=0 and t_end=999999 suggest full-video retrieval, but the description never confirms this), and result shape are all absent.

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%, so the description must compensate, and it does name all three concepts (video, t_start, t_end) and imply an inclusive range. However, it never specifies units or format for the numeric bounds, nor what video_sha256 actually identifies or where an agent obtains one, leaving real ambiguity.

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?

States a specific verb+resource pair ("Retrieve timestamped transcript segments") scoped to a single video and a time window, which is meaningfully more precise than the name alone. It does not explicitly contrast itself with sibling tools like search_corpus or get_shot_context, so the distinction must be inferred from the word "transcript".

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 statement of when to use this versus search_corpus (corpus-wide search) or get_shot_context (visual context around a moment). No prerequisites are given, such as whether the video must already be ingested via ingest_video, despite get_ingest_status being a sibling that implies an async pipeline.

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