switch

Show Media

show_media

Read-onlyIdempotent

Display the user's images inline — one or many. Users speak plainly and will NOT know asset ids; never ask for one, resolve it yourself. For "show me" or "show me my last image" call with NO arguments (shows the most recent image). For "show me my last 4 images / my last 10 pictures" pass count=N (returns a clean grid, up to 12). For a specific known image pass assetId. Renders a branded SwitchApp media card with a Download action per result; do not just print URLs. (Videos are not shown here — use list_my_videos and return the newest finished video's view_url, which plays.)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`count`	No	Optional. How many of the most recent images to show as a grid (default 1, max 12). Use when the user says "my last N images/pictures".
`assetId`	No	Optional. A specific image id (from list_my_assets, search_my_library, or show_generation). Omit to show the most recent image(s).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`asset`	No
`images`	No
`_widget`	No

TDQS

A5/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint), the description discloses important behavior: renders a branded SwitchApp media card with a Download action, instructs not to print URLs, and clarifies that asset IDs must be resolved autonomously. These details significantly aid an agent in executing correctly and align with the annotations.

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 longer than minimal but every sentence serves a purpose: front-loaded purpose statement, usage examples, output format note, and video alternative. No redundancy or filler—it is dense with actionable information while remaining readable.

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 the tool's complexity (multiple invocation modes, sibling distinctions, output behavior), the description covers all critical aspects: common user phrasings, parameter constraints (max 12), output format, and a clear pointer for videos. With an output schema present, return values are handled elsewhere, so nothing essential is missing.

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 input schema already provides 100% coverage for count and assetId, the description enhances this by mapping natural language to parameters ('my last 4 images' → count=4), defining the no-argument default, and clarifying that assetId comes from specific sibling sources. This goes well beyond the schema's static descriptions.

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's function with a specific verb ('Display') and resource ('user's images inline'). It also distinguishes from siblings by explicitly noting that videos are not shown and referencing list_my_videos for that purpose, making the tool's scope unambiguous.

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 usage patterns: no arguments for 'show me', count for 'my last N images', and assetId for a specific known image. It also names an alternative tool for videos and instructs the agent to never ask for asset IDs, instead resolving them itself—clear guidance on when and how to use this tool.

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.

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