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by ai.switchapp

Full Video Analysis

analyze_video_report

Run the FULL Switch Vision analysis on a video, the same premium report the Video Analysis page produces: it watches AND listens in three forensic passes and returns a structured report with every category: overview (scores and takeaways), a second by second timeline, audio, visual craft, story and retention, speech transcript, ready to run recreation prompts, and metadata. Pass video_url (a public https video URL, YouTube included) OR one of your own Switch video ids. For an external file also pass duration_seconds (YouTube and your own videos are measured automatically) because the analysis is billed per second of the file, 3 tokens per second with a 30 second minimum. Re-running the same video and question returns the existing report without charging again. Optional question focuses the analysis. Returns a report_id right away; poll get_vision_report until status is succeeded (a few minutes). If it cannot finish, your tokens are returned automatically. For one quick question about a video use analyze_video instead; this tool is the full paid report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoOptional. Re-running the same video and question returns the existing report without charging again; pass true to force a fresh, freshly billed analysis.
questionNoOptional. Something to pay special attention to.
video_urlYesA public https video URL (YouTube ok), OR one of your own Switch video ids.
duration_secondsNoLength in seconds. Required for external files; YouTube and your own Switch videos are measured automatically.

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnlyHint=false annotation, the description discloses significant behavioral traits: it bills per second with a 30-second minimum, re-running the same video/question is not charged again (with force to override), it returns a report_id immediately rather than a synchronous result, and tokens are returned automatically if it cannot finish. This is exactly the context an agent needs to manage expectations.

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?

The description is dense but every sentence earns its place. It front-loads the core purpose, then logically flows through inputs, billing, re-run behavior, return type, polling, failure handling, and alternatives. No filler words; the length is justified by the tool's complexity.

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?

Given no output schema, the description adequately covers return semantics (report_id + polling via get_vision_report) and lists the report's content categories. It also explains billing constraints, the cached-result behavior, and the distinction from the lighter analyze_video tool. This is a well-rounded description for a complex tool.

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

Parameters5/5

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

Although the schema already documents all four parameters, the description enriches meaning by explaining duration_seconds is required for external files but auto-measured for YouTube/Switch videos, and that the 'force' parameter overrides the default cached-report behavior. This clarifies real-world usage beyond the schema's field-level definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool runs the full Switch Vision analysis on a video, producing a premium report with specific categories (overview, timeline, audio, visual craft, story/retention, transcript, prompts, metadata). It explicitly contrasts with the sibling analyze_video tool, making its unique purpose unmistakable.

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

Usage Guidelines5/5

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

Provides explicit when-to-use guidance: use for full paid report vs. analyze_video for quick questions. Details input options (public URL or Switch video id), duration_seconds requirement for external files, billing rules (3 tokens/sec, 30s minimum), and the polling flow via get_vision_report. This is comprehensive and directly actionable.

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
Disambiguation2/5

Several tools occupy nearly identical semantic ground: apply_iphone_realism and apply_ugc both describe casual phone-shot looks, upload_media and upload_reference_asset both accept uploads, and analyze_video overlaps heavily with analyze_video_report. The many apply_* style tools are essentially one tool parameterized by style, so agents can easily select the wrong one.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case pattern such as generate_image, list_my_videos, get_editor_run, and upscale_video. A few outliers like voice, talking_avatar_video, and video_to_prompt do not use the same verb-first convention, but they are still readable and do not create significant confusion.

Tool Count1/5

At 55 tools, the surface is far beyond what is appropriate for an MCP server; many of these be collapsed or parameterized, especially the 10 apply_* style wrappers and several overlapping upload/status helpers. Even for a broad media platform, this scale forces a huge context window and makes selecting the right tool impractical.

Completeness2/5

The surface covers generation, media display, video analysis, and Editor workflows well, but there are obvious gaps in library lifecycle management: move_asset and create_folder are referenced in tool descriptions without being exposed, and there is no clean way to delete or reorganize media assets. Agents following the descriptions will try to call tools that do not exist.

Resources