reframe_videoReframe a video to a different aspect ratio (e.g. 16:9 master → 9:16 vertical) with smart subject tracking. Paid render; returns the served URL of the reframed video.
| Name | Required | Description | Default |
|---|---|---|---|
| video | Yes | the source video URL | |
| aspectRatio | Yes | the target aspect ratio |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With all annotations false, the description carries the burden of behavioral disclosure. It adds value by stating the operation is a paid render and returns a served URL, both critical behavioral traits. The mention of smart subject tracking further informs the agent. No contradiction 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the core action, and includes an illustrative example plus key notes (paid, output). It is concise with no redundant wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no output schema, the description covers the purpose, the cost implication, and what is returned (served URL). It is sufficiently complete for an agent to call correctly, though it lacks explicit guidance on when not to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% since both parameters have descriptions in the schema. The tool description adds no extra meaning beyond the example aspect ratio transformation; it does not describe parameter formats or edge cases. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('reframe') and resource ('video'), plus the exact transformation (aspect ratio change) with an example. This clearly distinguishes it from siblings like clip_video or edit_video, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes the core use case clear (reframing to a different aspect ratio) but does not explicitly contrast with alternatives or state when not to use. The 'paid render' note implies a cost consideration but provides no explicit routing to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.