top_videos
Get top performing tracked videos. Free.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (1-100, default 10) |
Reel25 — Video Analytics for TikTok, Instagram & YouTube
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits on its own. It only states 'Free,' which is vague, and does not explain what 'top performing' means, any limitations, whether it is read-only, or what the response looks like.
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 very short and front-loaded, stating the core function immediately. The 'Free.' note is brief but may inform cost behavior, making it acceptable though minimal.
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?
The tool has a single optional parameter and no output schema, but the description is still incomplete. It doesn't specify what is returned, how 'top performing' is determined, or any limitations, leaving the agent to guess.
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 schema covers 100% of parameters, including a description for the 'limit' parameter. The description adds no extra meaning beyond this, so the baseline of 3 applies.
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 gets top performing tracked videos, using a specific verb and resource. It distinguishes itself from list_videos by focusing on 'top performing,' though it doesn't explicitly contrast with all siblings.
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 guidance is provided on when to use this tool versus alternatives. The word 'Free' hints at cost implications but doesn't clarify usage context or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
A3.5/5.0
Disambiguation5/5
Every tool targets a distinct resource and action: account vs video vs folder vs analysis vs radar. Even similar tools like analyze_video and get_analysis are clearly separated by creation vs retrieval.
Naming Consistency4/5
Most names follow a verb_noun pattern (track_video, get_account, create_folder). Minor deviations like 'growth_trends' and 'radar_history' are noun phrases but remain readable and predictable.
Tool Count4/5
26 tools is slightly above the ideal range, but the server covers a broad domain with distinct sub-areas (accounts, videos, folders, analytics, AI analysis, radar), so the count is justified rather than bloated.
Completeness4/5
Core lifecycle operations are covered: track/untrack accounts and videos, list/get details, analytics, folders, and AI analysis. Minor gaps exist (no delete folder, no remove-from-folder), but they don't break primary workflows.