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Glama

search_video

Search across video transcripts with natural language to retrieve relevant timestamped chunks, optionally filtered by video name.

Instructions

Semantically search across all indexed video transcripts.

Returns the most relevant timestamped transcript chunks for a given query. You can optionally filter to a specific video by name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query (e.g. "what did they say about pricing?")
n_resultsNoNumber of results to return (default: 5, max recommended: 10)
video_nameNoOptional filename to restrict search to one video (e.g. "lecture.mp4")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the return shape ("timestamped transcript chunks") which is useful. However, it omits whether results are ranked, pagination/truncation behavior, and the requirement that the video be indexed first.

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?

Three short sentences, front-loaded with the core action and immediately followed by the return value. Little waste, though the optional-filter sentence is slight filler given the schema already documents video_name.

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?

An output schema exists so return values need not be explained, and the description covers the core operation. It still lacks prerequisites (indexing state) and any routing hint against ask_video, leaving an agent to guess which tool answers a transcript query.

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 all three parameters are already documented in the schema, making 3 the baseline. The description adds only a light restatement of the video_name filter and does not clarify query syntax or ranking/pagination semantics beyond what the schema says.

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 and scope: "Semantically search across all indexed video transcripts." That is clear and concrete, but it does not differentiate this tool from the sibling ask_video, which an agent could easily confuse with a semantic transcript search.

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?

The description says the video_name filter is optional but gives no when-to-use guidance, no prerequisites (e.g. transcripts must already be indexed via ingest_video), and no comparison to ask_video or list_videos. Callers must infer the search-vs-answer distinction themselves.

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