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Hermoso

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JavaScript
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Generate text

generate_text

Generate ad copy, hooks, scripts, rewrites, or brainstorms with Claude, Gemini, GPT, Llama, and DeepSeek writing models. Prompt-only; raw mode returns unstyled model 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 (a credit or two by length).

Input Schema

TableJSON 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

A4.7/5.0
Behavior4/5

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

Annotations are minimal (readOnlyHint: false, openWorldHint: false, etc.), so the description carries the burden. It discloses that the default behavior applies a house system prompt (to act as a copywriter) and that raw:true disables that, which is critical for the agent to know. It also mentions that the tool is paid (credits), adding cost context not present in annotations. This goes beyond the annotations, though it doesn't detail other side effects (e.g., whether it's idempotent), but the key behavioral traits are well covered.

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

Conciseness5/5

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

The description is concise but information-dense, with all key points front-loaded: what it does, when to use alternatives, the raw behavior, and model selection. Every sentence adds value, and it avoids redundancy with the schema. It's well-structured for quick parsing by an agent.

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?

Given the tool's moderate complexity (3 params, no output schema), the description is complete. It covers the tool's scope, usage conditions, parameter behavior, and cost implications. There's no obvious missing information an agent would need to invoke it correctly, especially since schema covers 100% of parameters.

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 description coverage is 100%, so baseline is 3. The description adds value by clarifying the meaning of the raw parameter (default true, when to use it) and the model parameter (a writing-model id, omit for default). It also notes that omitting model uses the Claude orchestrator. These clarifications supplement the schema, earning a 4.

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's purpose: text generation from a catalog of writing models, with specific use cases listed (ad copy, hooks, scripts, rewrites, brainstorms). It explicitly distinguishes from sibling tools like plan_ad and render_ad, saying it is prompt-only and not for ad assembly, which helps differentiate it from the numerous other content tools.

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?

The description provides explicit guidance on when to use this tool: for prompt-only text generation, and when not to use it (for finished on-brand creative, use plan_ad -> render_ad). It also explains when to set the raw parameter (for non-marketing tasks) and how to select the model, giving clear direction on usage versus alternatives.

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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