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Dalea AI

Get video

get_video
Read-onlyIdempotent

Get an own post by platformVideoId (optional platform disambiguates). Includes full caption, applicable metrics, classificationSources, saved analysis with transcript and timed segments, source/evidence/language/analyzedAt, nextStep and comparison with the same account video baseline (minimum 5 videos with views). Comparison engagement rates are ratios; engagementRatePct is percent. analysisStatus distinguishes ready, transcript_only, not_analyzed, failed and not_applicable. Legacy analysis language/evidence can be unknown. Read-only; does not generate missing analysis. Images/carousels have null views, engagement and watch time; inspect metricAvailability.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandIdNo
platformNo
platformVideoIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint/idempotentHint true and destructiveHint false, yet the description adds substantial context beyond them: the full returned payload, analysisStatus state enumeration, legacy unknown language/evidence, null handling for images/carousels, the 5-video minimum for baseline comparison, and the explicit boundary that it does not generate missing analysis.

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?

Front-loaded with the core action and identifier before the dense return-shape detail. The middle sentences are information-heavy but each adds value given the absence of an output schema, though the run-on phrasing is denser than necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return-value burden and does so thoroughly: it enumerates the key fields, statuses, comparison semantics (ratios vs engagementRatePct), and edge cases. An agent has enough to call it and interpret results correctly.

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 0%, so the description must carry param meaning. It clarifies platformVideoId as the identifying key and platform as an optional disambiguator, but brandId is never mentioned, leaving one of three parameters undocumented.

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 and resource ("Get an own post by platformVideoId") and scopes it to the caller's own content, which helps distinguish it from catalog/search siblings. It does not explicitly name a differentiating sibling like get_content or get_catalog_video, so an agent must infer the boundary from "own post" and the platformVideoId key.

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 phrase "own post" and "Read-only; does not generate missing analysis" imply when this tool applies and hint that analysis generation is a separate concern. However, no alternative is named (e.g., analyze_video for generating analysis, get_content for other content types), leaving routing to inference.

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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