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Edit a video clip

edit_video

EDIT/transform an existing video clip with a natural-language instruction (video-to-video) — KEEPS the original motion, timing and edit, changes the subject/setting/style. Use for 'change the background to a city', 'make it nighttime', 'restyle it as claymation', 'swap the product'. Best on 3–10s clips. NOT for mechanical cuts, trims, end cards or watermarks (use post_edit — seconds, ~2 credits, no AI model), NOT for making a new video (generate_video / render_ad), NOT for translating the spoken track (dub_video) and NOT for putting a saved creator's face on the motion (recast_motion). Paid render; returns the served URL of the edited clip.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoYesthe source video URL (from a previous render, a job result, or list_library)
elementsNoOPTIONAL identity/product grounding (≤4): a creator portrait or the real product photo, so the edit restores the REAL thing instead of re-inventing it. Describe each one in the instruction. Leave out for a plain restyle
keepAudioNodefault true — keep the source clip’s audio track. Set false to return the edit silent
instructionYesthe exact transformation to apply, in the user’s own words

TDQS

A4.3/5.0
Behavior4/5

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

Annotations are sparse (readOnlyHint=false, destructiveHint=false, openWorldHint=false), so the description carries most of the disclosure burden. It discloses that the operation keeps the original motion/timing, is a paid render, operates best on short clips, and returns the served URL of the result. It could additionally note idempotency or failure behaviors, but the disclosed traits (cost, preservation of motion, length range, output URL) are substantial beyond what annotations provide.

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?

The core scoping ('KEEPS original motion, changes subject/setting/style') is front-loaded, followed by usage examples and then a compact series of exclusions. It is long, but this density is justified by the large sibling set it must disambiguate against (post_edit, generate_video, render_ad, dub_video, recast_motion). A few phrases like 'so the edit restores the REAL thing' could be trimmed, but nothing is wasted.

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?

For a 4-parameter tool with no output schema and sparse annotations, the description provides the key operational context: what the tool preserves, what it changes, best-length constraint, cost implication, and the result URL. It omits explicit pagination/timeout/credit-exactness details and does not restate the return value beyond the URL, but for this tool's complexity the coverage is close to complete.

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%, so the schema already documents all four parameters thoroughly, including the elements object's 'frontal' and 'refs' fields and the keepAudio default. The description adds marginal value by explaining the purpose of 'elements' ('so the edit restores the REAL thing') and the silent-return behavior of keepAudio=false, but the heavy lifting is done by the schema — earning the baseline 3.

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?

Phrased with an explicit verb and resource ('EDIT/transform an existing video clip'), a precise definition of the video-to-video behavior ('KEEPS the original motion, timing and edit, changes the subject/setting/style'), and concrete examples ('change the background to a city', 'restyle it as claymation'). It distinguishes itself unambiguously from generate_video, post_edit, and dub_video, whose names it shares the domain with.

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?

Names at least four alternatives and the exact conditions that select each: post_edit for mechanical cuts/trims (with cost and credit hints), generate_video/render_ad for new videos, dub_video for translation, and recast_motion for face transfer. Also bounds applicability ('Best on 3–10s clips'). No inference is required from the agent.

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.