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Hermoso

by io.github.hermoso-ai

Product sizzle (music-led)

product_sizzle

Render an 18-30s music-led PRODUCT SIZZLE: ONE 15s Seedance 2.0 hero clip of the product, diced into fast cuts and intercut with typeset spec/CTA cards on a brand-coloured grain background, mixed to a music bed. Faceless by design — no people, no voiceover, no spoken lines; the cards carry every word, so nothing is left to a video model's spelling. Pass a real packshot as refImage or the label will not be yours. EXPENSIVE — the hero clip is the only paid leg and it is a full 15s Seedance render: ≈1,040 credits at the DEFAULT 1080p, ≈470 at 720p, ≈220 at 480p, ≈4,130 at 4k (call hermoso_capabilities for the live seedance-2 per-duration numbers; the dicing and the cards are free, and the music bed is already included in the quoted figure). Confirm the spend with the user before calling. For a talking/UGC ad use render_ad or generate_avatar; for a cheap deterministic format use make_template_ad.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ctaNoclosing CTA line, ≤30 chars
specsNoup to 4 spec lines for the typeset cards, ≤26 chars each
promptYeswhat the sizzle should show — the product, the setting, the look
secondsNofinished length, clamped to 18-30s (default 25). The PAID hero render is always 15s regardless — this only changes how the cuts and cards are packed
refImageNoproduct packshot URL that anchors the real label — strongly recommended
brandNameNobrand name on the cards — defaults to the workspace brand
musicMoodNomusic-bed mood, e.g. driving / cinematic / upbeat
resolutionNohero-clip resolution and therefore the whole cost — DEFAULT '1080p' (≈1,040 credits); '720p' ≈470, '480p' ≈220, '4k' ≈4,130
aspectRatioNo'9:16' default; anything the seedance-2 catalog entry does not list falls back to 9:16

TDQS

A4.7/5.0
Behavior5/5

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

Annotations are minimal (all false), so the description carries the full burden. It discloses significant behaviors: the exact cost breakdown per resolution (≈1,040 credits at 1080p, etc.), the fact that the hero render is always 15s regardless of the 'seconds' parameter, the faceless design (no people/voiceover), and the dependency on a real packshot. It also warns to call hermoso_capabilities for live numbers. This is highly transparent and consistent with annotations.

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 lengthy but arguably justified; each sentence provides actionable information (structure, faceless design, refImage warning, cost breakdown, alternative routing). It is front-loaded with the core purpose and then builds out critical details. Not a single sentence is filler, though it could be trimmed slightly without losing value.

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 complex, cost-heavy tool, the description is remarkably complete: it covers the output structure, the paid/free components, exactly how cost scales, the need for user confirmation, and alternatives. It lacks an explicit statement of the return value (e.g., whether it returns a job ID or video URL), but given the tool's nature, this is a minor gap. Overall, it equips the agent to call correctly.

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 baseline is 3. The description adds meaningful context beyond the schema: it explains the cost implications of resolution, clarifies that 'seconds' only affects packing not the paid render, and stresses the importance of refImage for label accuracy. This elevates the value above baseline.

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 it renders a music-led product sizzle with a specific structure: a 15s Seedance 2.0 hero clip diced into fast cuts intercut with typeset cards. It differentiates from sibling tools by naming alternatives (render_ad, generate_avatar, make_template_ad) and what they are for.

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?

Explicitly states when NOT to use this tool: 'For a talking/UGC ad use render_ad or generate_avatar; for a cheap deterministic format use make_template_ad.' It also instructs to confirm spend with the user before calling, providing clear usage context.

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.

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