Skip to main content
Glama

analyzeVideoSet

Batch analyze up to 20 YouTube videos to extract transcripts, sentiment, comments, and engagement patterns with item-level error handling and full provenance.

Instructions

Run multiple analyses across a video set with partial success, item-level errors, and provenance. [~5-20s, scales with video count]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoIdsOrUrlsYes
analysesYes
commentsSampleSizeNo
transcriptModeNo
dryRunNo
Behavior3/5

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

With no annotations provided, the description carries the full disclosure burden. It successfully communicates critical behavioral traits: partial success handling, item-level error reporting, provenance tracking, and performance characteristics (~5-20s scaling). However, it omits mutation semantics (whether results are cached/persisted), authorization requirements, or side effects.

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?

Every sentence earns its place. The first sentence densely packs purpose, scope, and behavioral traits (partial success/errors/provenance). The bracketed timing annotation [~5-20s, scales with video count] is efficiently appended. No redundancy or filler text.

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?

For a tool with moderate complexity (5 parameters, batch operations, multiple analysis types) and zero schema descriptions, the description provides adequate but incomplete coverage. It establishes the operation mode and performance but leaves parameter semantics and return value structure (no output schema exists) undocumented.

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

Parameters2/5

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

Schema description coverage is 0%, requiring the description to compensate significantly. While 'multiple analyses' loosely hints at the analyses parameter enum, the description provides no guidance on the other four parameters (videoIdsOrUrls constraints, dryRun purpose, transcriptMode options, commentsSampleSize limits) or valid analysis type values.

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 uses specific verb 'Run' and resource 'analyses across a video set', clearly indicating batch processing of multiple videos. The mention of 'partial success, item-level errors' adds distinctive behavioral context. However, it does not explicitly differentiate from siblings like analyzePlaylist (playlist-based vs. explicit video ID list) or inspectVideo (single vs. batch).

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?

No explicit guidance on when to use this tool versus alternatives. Given the numerous siblings (analyzePlaylist, inspectVideo, readTranscript, etc.), the description fails to specify selection criteria such as 'use this when you have specific video IDs rather than a playlist' or 'use this when you need multiple analysis types in one call'.

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/thatsrajan/vidlens-mcp'

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