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Recast motion

recast_motion

Motion transfer: re-perform a reference video's motion with a different person/character (supply their image). The reference clip drives the movement; the image supplies the identity. Paid render.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesthe actor/character image URL (who should appear)
videoYesthe reference video whose motion to re-perform
promptNooptional scene/style guidance
orientationNowhich aspect to keep: the video's (default) or the image's

TDQS

A4.2/5.0
Behavior3/5

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

All annotations are false (readOnlyHint, destructiveHint, etc.), so the description carries the full burden of behavioral disclosure. It adds the key fact that this is a 'Paid render,' which is useful. However, it doesn't describe the output (presumably a video), any processing requirements, or potential side effects. Given the lack of annotation support, the description could be more explicit about the operation's nature (e.g., it creates a new video).

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 three succinct sentences with zero waste. It front-loads the purpose, immediately clarifies the input roles, and then notes the cost. Every sentence earns its place, making it highly scannable for an agent.

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?

Given the tool's complexity, the description covers the core interaction (motion transfer with identity from image) and the paid aspect. It does not specify the output format or any limitations, but with no output schema and minimal annotations, it still provides enough for an agent to understand the fundamental use case. The omission of details like processing time or supported video formats is a minor gap.

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

Parameters4/5

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

Schema coverage is 100%, so all parameters have descriptions. The tool description adds significant meaning beyond that by explaining the functional roles: 'The reference clip drives the movement; the image supplies the identity.' This clarifies how image and video interact, which the schema descriptions do not convey. The description does not address the optional prompt and orientation, but the schema covers them adequately.

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 identifies the tool's purpose: 'Motion transfer: re-perform a reference video's motion with a different person/character (supply their image).' The verb 're-perform' and the explicit resource ('reference video's motion') make it distinct from siblings like edit_video or generate_video. The roles of image and video are clearly separated, leaving no ambiguity about what the tool does.

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

Usage Guidelines4/5

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

The description gives clear context for when to use this tool: whenever you need to transfer motion from a reference video to a new identity. It doesn't explicitly mention alternatives or when-not-to-use, but the unique function (motion transfer) is self-evident against the sibling list. The absence of explicit exclusions keeps it slightly short of a 5.

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

A3.7/5.0
Disambiguation2/5

With 293 tools, the surface is enormous and many tools have overlapping purposes—multiple posting tools (post_to_meta, post_to_linkedin, schedule_post, etc.), multiple analytics tools per channel, and several search tools (search_meta_ads, search_instagram, search_reddit...). While each description is detailed, the volume makes it difficult for an agent to reliably distinguish between similar tools without careful reading, leading to frequent misselection.

Naming Consistency4/5

The naming is largely consistent with a verb_noun pattern (post_to_*, list_*, create_*, delete_*, update_*, manage_*). There are clear families for major operations. A few outliers like 'google_business_account', 'hermoso_capabilities', and 'store_get' break the pattern, but the overwhelming majority follow a predictable structure, making navigation somewhat easier.

Tool Count1/5

293 tools is far beyond any reasonable scope for a single MCP server, even for a comprehensive marketing platform. The calibration guide flags 50+ as an extreme mismatch, and this is nearly six times that threshold. Such a large surface overwhelms context windows, increases the probability of misselection, and makes it impractical for agents to learn or use effectively.

Completeness4/5

The tool set covers a vast domain: ad creation and rendering, posting across nine+ social channels, analytics and reporting, file management (Drive/OneDrive), competitor research, brand management, and more. It appears to provide CRUD and lifecycle coverage for most resources. While there may be minor gaps given the immense scope, the overall coverage is impressively comprehensive.