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Hermoso

by io.github.hermoso-ai

Research ads

research_ads
Read-only

Open-ended ad research that needs JUDGMENT across platforms — comparisons, "what angle is working", "who else is doing this", anything where the right sources are not known up front. It is an agentic loop (several rounds of library pulls plus a written synthesis) and typically takes 30-60 seconds, so it is the WRONG tool for a question that names its own answer. For one named brand’s live ads use pull_competitor_ads; for one keyword or one advertiser on Meta use search_meta_ads — both are a single call and return in a few seconds. Spends credits — an agentic loop, so a handful rather than the one-call cost of a targeted search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNobrand name or profile object to tailor the research to; omit to use the workspace’s saved brand
queryYeswhat to research, e.g. "the longest-running protein-pancake ads on Meta"

TDQS

A4.6/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true and openWorldHint=true, and the description adds crucial behavioral context: it is an agentic loop taking 30-60 seconds, and it spends credits (a handful rather than one-call cost). This goes well beyond the annotations and sets accurate expectations for latency and cost.

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 efficient, front-loading purpose and contrasting with alternates. It is slightly verbose but every sentence adds value (cost, latency, alternatives). No waste, though it could be marginally tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an open-ended research tool with no output schema, the description addresses the essential aspects: what it does, when to use vs. alternatives, behavior (agentic loop, latency, cost), and parameter intent. An agent has everything needed to decide and invoke correctly.

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 both parameters. The description restates the brand parameter's purpose but doesn't add materially new semantics beyond the schema. Baseline 3 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 clearly states the tool is for open-ended ad research requiring judgment, gives concrete examples, and explicitly distinguishes it from sibling tools like pull_competitor_ads and search_meta_ads. The verb and resource are specific, and the scope is unambiguous.

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?

It explicitly states when to use (open-ended, judgment-driven, unknown sources) and when not (when the question names its own answer). It names alternatives and the exact conditions for choosing them, leaving no ambiguity.

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.

Resources