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Fast Video Cataloger

Official

Search scenes

search_scenes
Read-only

Find specific moments inside cataloged videos by matching scene keywords or free text, returning thumbnail IDs, video IDs, and timecodes for each result.

Instructions

Search individual scenes inside the user's Fast Video Cataloger videos, rather than whole videos. This is how you find the moment inside a video: each result carries a thumbnail id, the video it belongs to, and its timecode. Pass the thumbnail id to get_scene_image to look at it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results. Defaults to 40.
queryNoFree text to match against scene keywords.
videoIdNoRestrict the search to one video by its id.
keywordsNoComma-separated keywords. A scene matches if it is tagged with ANY of them, not all of them - listing more keywords widens the search rather than narrowing it. Scene keywords come from object detection, so they are concrete things that appear in the frame ('person', 'laptop', 'car'), not themes or descriptions.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv10.4.1

TDQS

A4/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=true, so the description adds valuable context by disclosing the result fields (thumbnail id, video, timecode) and the chaining step to get_scene_image. It does not cover auth or rate limits, but the lower bar with annotations makes this sufficient.

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?

Three tightly written sentences, front-loaded with the scope distinction, then result shape, then next action. No waste.

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?

Given no output schema, the description adequately explains what results contain and how to use them. It could mention the semantic-search sibling to fully disambiguate, but is otherwise complete for this search tool.

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 four parameters are already clearly documented. The description does not add meaning beyond what the schema provides, making the baseline 3 appropriate.

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 (search) and resource (individual scenes) with scope (inside the user's videos) and explicitly contrasts with whole-video search. It does not name the semantic-search sibling, so it falls short of a 5.

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

Usage Guidelines4/5

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

Gives clear context for when to use it (to find the moment inside a video) and an implicit exclusion (rather than whole videos), plus the next tool to call (get_scene_image). It does not mention alternatives like search_scenes_semantic.

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