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creative-tagger-mcp

creative-tagger-mcp

by stephenlavender
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

recommend

Provides concrete creative recommendations for strategic advertising questions by analyzing brand context and taxonomy values.

Instructions

Ask the Creative Strategist a question, grounded in the user's library + brand context. The strategist auto-loads patterns from prior analyses and any saved brand voice/audience/anti-patterns for the brand, then answers with concrete creative recommendations using taxonomy values. Use this for open-ended strategic questions ('what should I test next', 'how should I approach Q4', 'what kind of UGC would work for this audience').

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": {
      "brand_name": "<brand_name>",
      "question": "<question>"
    },
    "name": "recommend"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYesThe strategic question
brand_nameYesBrand to ground the recommendation in

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.2

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool auto-loads patterns from prior analyses and brand voice/audience/anti-patterns, and answers with concrete recommendations. This adds useful behavioral context beyond the input schema, though it does not mention authorization or rate limits.

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 and front-loaded, with two sentences that efficiently convey purpose, grounding, and usage examples. No extraneous information, and every part earns its place.

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?

Given the tool's complexity (AI-driven strategist), the description adequately explains what it does and what context it uses. There is no output schema, but the description mentions the nature of the answer ('concrete creative recommendations using taxonomy values'). It could benefit from mentioning return format or limitations, but it is largely complete.

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 description coverage is 100%, and the descriptions of 'question' and 'brand_name' are clear. The main description adds context (e.g., 'strategic question', 'ground the recommendation') but does not significantly enhance parameter semantics beyond the schema. Baseline 3 is appropriate.

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: to ask the Creative Strategist a question grounded in the user's library and brand context, and receive concrete creative recommendations using taxonomy values. It distinguishes from siblings like analyze_creative or predict_creative by focusing on open-ended strategic questions.

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

Usage Guidelines4/5

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

The description provides explicit usage guidance by listing example questions and explaining the tool's grounding in brand context and prior patterns. It implicitly differentiates from data querying tools but does not explicitly state when not to use it or contrast with specific sibling tools.

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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