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Get real analytics for a video

get_video_analytics
Read-onlyIdempotent

Retrieve a Vimeo video's views over time, unique viewers, finish rate, and view sources to measure performance and audience behavior.

Instructions

Get real analytics for a video: views over time, unique viewers, finish rate and where views came from. This is Vimeo's reporting API and it needs a paid plan. On a free account it returns a plain explanation rather than an error.

Input Schema

NameRequiredDescriptionDefault
to_dateNoEnd date, YYYY-MM-DD. Defaults to today.
video_idYesVideo id.
dimensionNoHow to break the numbers down.time
from_dateNoStart date, YYYY-MM-DD. Defaults to 30 days ago.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv2.0.0
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv1.0.1

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint=false, so safety is covered. The description adds genuinely non-obvious behavior: this is the reporting API, it needs a paid plan, and on free accounts it degrades to an explanation string rather than raising an error. That last detail meaningfully changes how the agent interprets a response, though return shape/pagination is not described.

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?

Two tight sentences, metrics front-loaded and the paid-plan caveat second. No filler, no repetition of the title, every clause earns its place.

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?

With no output schema, the description must convey what comes back, and it does by listing the four metric families. Combined with the paid-plan/free-account caveat, an agent has enough to call and interpret it, though the response format and date-range interaction are only implied.

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%, including the enum and date defaults, so the schema carries the parameter burden. The description's metric list loosely aligns with the 'dimension' breakdown (e.g., where views came from) but adds no formats, constraints, or semantics beyond the schema; baseline 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?

Specific verb (get) plus resource (video analytics) with an enumeration of what is returned: views over time, unique viewers, finish rate, and view sources. The word 'real' hints at a distinction from a lighter stats tool, but it never names the sibling get_video_stats, so the agent must infer the split.

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 surfaces a real prerequisite (requires a paid plan, free accounts get an explanation rather than an error), which is useful context for when calls will succeed. However it gives no explicit when-to-use versus get_video_stats or other reporting alternatives, so routing between the two analytics-ish siblings is left to inference.

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