analyze_videoBreak a video ad down into its structure: the verbatim transcript (voiceover + on-screen text) with a beat list, plus duration and sampled frame timestamps. Use to study a reference/competitor ad before remixing its structure. Costs ~a transcription call.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | the video URL (a served /generated/ path or a public http(s) video) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds value by disclosing a cost ('Costs ~a transcription call') and noting the output includes 'sampled frame timestamps' (not all frames). This goes beyond the annotations and gives the agent useful expectations. No contradiction.
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 two sentences with zero waste. The first sentence states the action and outputs; the second gives the use case and cost. All key information is front-loaded, and no unnecessary fluff appears.
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 single-parameter tool with clear annotations, the description covers the purpose, outputs, use case, and cost. It does not mention output format specifics or error conditions, but those are minor given the tool's simplicity. It is sufficiently complete for an agent to call correctly.
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?
The schema description for 'url' is already detailed ('a served /generated/ path or a public http(s) video'), and schema coverage is 100%. The tool description does not add any additional parameter-level guidance, so the baseline of 3 applies.
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 uses a specific verb ('Break down') and resource ('video ad'), and enumerates concrete outputs: verbatim transcript, beat list, duration, and sampled frame timestamps. It also positions the tool for a distinct use case (studying reference/competitor ads before remixing) that separates it from editing/generation tools like clip_video or generate_video.
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 explicitly states when to use the tool: 'Use to study a reference/competitor ad before remixing its structure.' This is clear, but it does not mention when not to use it or name specific alternatives, so it falls short of a 5. However, the context is enough for an agent to pick this over sibling video 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.