Skip to main content
Glama

Search Videos

search_videos

Search your private video library using natural language queries. Find specific moments, scenes, or actions and get matching video segments with timestamps.

Instructions

Semantic search across your private video library using natural language. Find specific moments, scenes, objects, or actions across all your indexed videos. Returns matching video segments with timestamps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query (e.g. 'person walking in the rain')
unique_idNoNamespace (default: 'default')
top_kNoNumber of results to return (default: 10)
tagNoFilter results by tag
video_nosNoLimit search to specific video numbers
Behavior3/5

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

With no annotations provided, the description carries full behavioral transparency burden. It discloses the tool performs semantic search and returns video segments with timestamps, but omits details like authentication requirements, rate limits, behavior for no results, or data access scope.

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 concise at two sentences, clearly front-loading the core purpose. It efficiently communicates the tool's function and output without unnecessary fluff.

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?

The description provides a basic understanding of the return format (video segments with timestamps) but lacks details on pagination, sorting, or error handling. Given the complexity of sibling tools and absence of output schema, the description is adequate but not fully complete.

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 has 100% description coverage, so the baseline is 3. The description adds value by framing the query parameter as 'natural language' but does not elaborate on other parameters (unique_id, top_k, tag, video_nos) beyond what the schema already provides.

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 clearly states the tool performs semantic search across a private video library using natural language, specifying what it finds (moments, scenes, objects, actions). However, it does not explicitly differentiate itself from sibling tools like search_audio or search_public, though the context of 'private video library' helps.

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

Usage Guidelines3/5

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

The description implies usage for natural language queries on indexed videos but lacks explicit guidance on when to use this tool versus alternatives (e.g., search_public for public videos, search_audio for audio). No 'when not to use' or prerequisites are mentioned.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/kennyzheng-builds/videoseek-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server