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Hermoso

Hermoso

Official
by hermoso-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Research ads

research_ads

Runs open-ended ad research across brands and platforms to compare angles, identify what is working in a market, and gather category ad examples.

Instructions

Use this when the user asks an open-ended ad question across several brands or platforms, such as comparisons, which angles are working in a market, or ad examples for a category. Do not use for one named brand’s current ads: pull_competitor_ads covers that. 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

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"

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.161
    • changedOutput schema / (root)
      Previous value: -{
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "additionalProperties": false,
-  "properties": {
-    "actions": {
-      "description": "follow-up actions the research loop suggested"
-    },
-    "reply": {
-      "description": "the research summary",
-      "type": "string"
-    },
-    "results": {
-      "description": "the found ads/videos (normalized card objects with served URLs)",
-      "items": {},
-      "type": "array"
-    }
-  },
-  "type": "object"
-}New value: +null
  2. Addedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Goes well beyond the annotations: discloses that it runs an agentic loop of several library pulls plus a written synthesis, takes 30-60 seconds, and spends a handful of credits rather than a single-call cost. This aligns with readOnlyHint=false and openWorldHint=true and adds the operational detail an agent needs to decide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded and useful, but repetitive: the 'one named brand → pull_competitor_ads' rule appears twice, and 'agentic loop' / credit cost is restated three times. Trimming the duplication would lose nothing.

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?

No output schema exists, and the description compensates by saying the output is a written synthesis from multiple pulls, along with latency and cost. For a two-parameter, expensive, long-running research tool, an agent has everything needed to select and invoke it 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 brand and query are already documented. The description adds value by characterizing the kind of query that fits (open-ended, non-self-answering) and by warning that a question naming its own answer is wrong for this tool, which sharpens interpretation of the required query parameter.

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?

States a specific verb+resource (agentic ad research across brands/platforms) and defines its scope by contrast: comparisons, market-level angle questions, category examples. It explicitly names pull_competitor_ads and search_meta_ads as the alternatives for narrower questions, so an agent can route without opening schemas.

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?

Gives both when-to-use (open-ended, multi-source, judgment-requiring questions) and when-not-to-use (one named brand's current ads → pull_competitor_ads; one keyword/advertiser on Meta → search_meta_ads), plus the cost/latency discriminator that rules it out for pinpoint questions.

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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