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Finish video

finish_video

Post-process an EXISTING rendered video (its served mp4 URL) with the proven direct-response 'reviewer' finish and/or a film-grain pass — no AI model, ~30s, a couple of credits. pills=true composites a header pill (e.g. '10/10 would buy again'), a brand-accent sub-pill, and 3-4 green-check proof pills cascading in on the beat (YOU author the copy: header ≤40 chars, sub ≤34, each point ≤44 — concrete real benefits, never fabricated stats). grain=true applies a subtle camera-grain finish that makes photoreal AI renders look phone-shot ('less AI') — works alone or with pills. Returns a NEW video; the original is untouched.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
subNoaccent sub-pill copy, ≤34 chars (usually the product/brand)
grainNodefault false — anti-AI film-grain finish
pillsNodefault true — set false for a grain-only pass
accentNobrand accent hex for the sub-pill
headerNoheader pill copy, ≤40 chars (required when pills is on)
pointsNo3-4 proof points, ≤44 chars each
videoUrlYesthe served URL of the video to finish (from a previous render/job)

TDQS

A4.5/5.0
Behavior5/5

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

Annotations provide no hints (all false), so the description carries full responsibility. It discloses the non-destructive nature ('original is untouched'), the output (a NEW video), the approximate cost/time ('a couple of credits', '~30s'), and explicitly that no AI model is involved. It details the effects of pills and grain, including the exact character limits for copy (≤40, ≤34, ≤44) and the instruction to use 'concrete real benefits, never fabricated stats.' This is thorough and transparent for an operation with side effects.

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 description is dense but not bloated. It front-loads the core action ('Post-process an EXISTING rendered video') and then details the two relevant parameters. The character limits and examples are packed efficiently. It's about 100 words, which is appropriate for a tool with this complexity. It could be trimmed slightly (e.g., the example '10/10 would buy again' is illustrative but not essential), but overall it earns its length.

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 has 7 parameters and two distinct modes, the description covers the main behavioral distinctions and the output format. It does not explain every parameter (but schema does, at 100% coverage), and it does not explicitly discuss failure modes or error handling. However, for a typical agent that just needs to know what to provide and what to expect, it is complete. The only minor omission is the exact relationship between pills and grain (it says grain works alone or with pills, but not that pills can exist without grain — implied by 'pills=true' default). Overall, it's sufficient for correct invocation.

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% (every parameter has a description), so baseline is 3. The description adds meaningful context beyond the schema: it explains the purpose of pills (composite a header, sub-pill, and proof pills), the effect of grain ('less AI' look), and how they interact. It also clarifies that 'header' is required when pills are on (though the schema already says that). The copy constraints are given in the description, enhancing the schema's simple '≤40 chars' notes. This is more than just restating schema, so a 4 is appropriate.

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 opens with a specific verb ('Post-process') and resource ('an EXISTING rendered video'), then immediately states the two finishes ('reviewer' pills and film-grain). It explicitly says 'Returns a NEW video; the original is untouched,' which distinguishes it from mutating tools. The mention of 'no AI model' and the ~30s time frame further clarifies what this tool is not. It clearly differentiates from siblings like clip_video, edit_video, and reframe_video by focusing purely on adding overlay/grain to an already-rendered video.

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 clearly states the use case: post-processing an existing rendered video, and explains the two modes (pills, grain) and that they can be combined. It implies when to use it (after a render) but does not explicitly contrast it with alternatives like edit_video or clip_video. It does say 'works alone or with pills' for grain, giving conditionality. Overall, the context is clear, but it lacks explicit 'use X instead' guidance for related tools.

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.