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generate_audiences

AI-generate audience profiles from a product (scrape + analysis) into the library. generated names already in the library are skipped, not overwritten. rate limited to 5 per hour. requires generate scope.

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": {
      "product_name": "<product_name>"
    },
    "name": "generate_audiences"
  }
}

Input Schema

NameRequiredDescriptionDefault
product_urlNoproduct website - scraped for extra context
product_nameYesproduct name
product_descriptionNowhat it does and who it's for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=false), it discloses that generation involves scraping plus analysis, that colliding names are skipped rather than overwritten, that there is a hard rate limit of 5 per hour, and that a 'generate' scope is required. Those are exactly the operational facts an agent needs before invoking.

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?

Four short clauses with no filler, and the most consequential facts (dedupe behavior, rate limit, scope) are packed in after the purpose statement. Nothing is repeated or padded.

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 three-parameter generation tool with no output schema, it covers mechanics, safety-relevant side effects (skip-not-overwrite), limits, and auth. The one gap is that it never indicates what comes back or how to retrieve the generated profiles afterwards, which the agent must infer from the library wording.

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 coverage is 100%, so the three parameters are already documented, including that product_url is scraped. The description reinforces the scrape-and-analyze use of those inputs but adds no format or precedence detail beyond the schema, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource ('AI-generate audience profiles'), the input source ('from a product (scrape + analysis)') and the destination ('into the library'). It is clearly distinguishable from a manual 'create_audience' by the AI-generation and scraping framing, though it never explicitly names that sibling.

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 only implied: you call it when you want AI-generated audiences from a product rather than hand-built ones. There is no explicit statement of when to prefer this over create_audience or list_audiences, and no prerequisites beyond the scope requirement are spelled out.

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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