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Hermoso

Hermoso

Official
by hermoso-ai
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JavaScript
Remote

Generate text

generate_text

Generate ad copy, hooks, scripts, rewrites, and brainstorm ideas across Claude, Gemini, GPT, Llama, and DeepSeek models; use raw mode for plain, non-marketing answers.

Instructions

Text generation against the writing-model catalog (Claude, Gemini, GPT, Llama, DeepSeek…) — ad copy, hooks, scripts, rewrites, brainstorms. Prompt-only, no ad assembly (for a finished on-brand creative use plan_ad -> render_ad). BY DEFAULT the model answers as a marketing copywriter (a short house system prompt is applied, which is what you want for ad copy); pass raw:true for a plain, unstyled answer from the model itself with NO system prompt at all. model = a writing-model id from hermoso_capabilities (omit for the default Claude orchestrator). Paid (billed at the writing model's token cost, by length).

Input Schema

NameRequiredDescriptionDefault
rawNoRAW MODEL ACCESS: send the prompt with NO Hermoso system prompt — the model answers as itself rather than as an ad copywriter. Use it whenever the ask is not marketing copy (analysis, code, extraction, a plain question). Default false: the copywriter framing is applied.
modelNoa writing-model id from hermoso_capabilities (a Claude / Gemini / GPT / Llama / DeepSeek id) — omit for the default
promptYesthe writing task / question

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.161
    • addedInput schema / properties / raw
      Added value: +{
+  "description": "RAW MODEL ACCESS: send the prompt with NO Hermoso system prompt — the model answers as itself rather than as an ad copywriter. Use it whenever the ask is not marketing copy (analysis, code, extraction, a plain question). Default false: the copywriter framing is applied.",
+  "type": "boolean"
+}
    • changedOutput schema / (root)
      Previous value: -{
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "additionalProperties": false,
-  "properties": {
-    "creditsUsed": {
-      "description": "credits billed for this generation",
-      "type": "number"
-    },
-    "model": {
-      "description": "the writing model label",
-      "type": "string"
-    },
-    "text": {
-      "description": "the generated text",
-      "type": "string"
-    }
-  },
-  "type": "object"
-}New value: +null
  2. Addedv0.1.15

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already flag openWorldHint=true, non-readonly and non-idempotent, so safety is covered structurally; the description adds real value beyond that by disclosing the paid billing model (token cost, by length) and the non-obvious default behavior that a house copywriter system prompt is injected unless raw:true. It does not describe the response shape or latency, which keeps it from a 5.

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?

Front-loaded with the capability and scope, then alternatives, then the raw/model defaults and pricing. It earns most of its length, but the dense parenthetical stacking ("(a short house system prompt is applied, which is what you want for ad copy)") costs readability against a single-sentence ideal.

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 3-param, no-output-schema generation tool, the description covers scope, defaults, model sourcing, and cost. The main omission is what the tool actually returns (a text string vs structured variants), which an agent might reasonably want to know despite the absence of an output schema.

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 the baseline is 3; the description goes slightly beyond by tying model to the hermoso_capabilities catalog and stating the default (Claude orchestrator), and by reinforcing that raw:true means no system prompt at all. Parameter behavior is well grounded, though it largely echoes the schema text.

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 (text generation against the writing-model catalog) and enumerates the concrete use cases (ad copy, hooks, scripts, rewrites, brainstorms). It explicitly distinguishes itself from plan_ad/render_ad for finished creative, so an agent can pick it out of a large sibling list without opening the schema.

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 says "prompt-only, no ad assembly" and routes to plan_ad -> render_ad for a finished creative, which is a named alternative plus the condition that selects it. It also gives a clear when-to-use rule for the raw flag and where to source model ids (hermoso_capabilities).

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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