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Draft the website text pack

draft_site_text

Propose the public website text of your restaurant — tagline, about paragraph, SEO title and SEO description — as a DRAFT change set. Only declared facts are allowed: no numbers, awards, links or markup; validation rejects anything else. The owner publishes from the admin panel.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "tagline": "<tagline>",
      "about": "<about>",
      "seo_title": "<seo_title>",
      "seo_description": "<seo_description>"
    },
    "name": "draft_site_text"
  }
}

Input Schema

NameRequiredDescriptionDefault
aboutYes
taglineYes
seo_titleYes
seo_descriptionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only supply the generic write/idempotency hints (readOnlyHint=false, idempotentHint=false). The description adds substantive behavior the annotations cannot express: the output is a DRAFT change set rather than a live publish, factual content is restricted, and validation will reject non-conforming input. That is exactly the kind of context the annotations leave open.

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?

Three tight sentences with zero filler: the draft nature is front-loaded, the content constraint follows, and the publish path closes it. Every clause carries information the agent needs.

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 four-parameter write tool with no output schema and only generic annotations, the description covers the essentials: what is produced, what is forbidden, and who completes the workflow. It does not describe what the returned draft change set looks like or whether re-drafting replaces a prior draft, which is the remaining gap for a non-idempotent tool.

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 0%, so the description has to carry the load, and it does: it maps each of the four parameters to a concrete meaning (tagline, about paragraph, SEO title, SEO description) and adds a content rule (no numbers, awards, links or markup) that appears nowhere in the schema. It stops short of noting relative lengths or tone guidance, so it is not fully compensatory.

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 (propose) plus the resource (public website text pack) and enumerates the four exact fields produced. It is clearly distinguishable from the nearby draft_product_description and draft_translation siblings because the resource domain is named explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: the description makes clear the output is a draft and that publishing happens later from the admin panel, which implicitly tells the agent not to expect live changes here. However, it never names an alternative tool or an explicit triggering condition, so the agent must infer when this is the right call.

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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