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Glama

Brandize

by me.brandize

buy_logo_variation

Purchase one of the alternate designs from generate_logo_variations instead of the original logo. Priced the same as the PREMIUM tier. Mints a short checkout link; once paid, the chosen variation becomes the job's deliverable — poll get_logo_result with the same jobToken to retrieve it. This is a paid purchase: you must obtain the user's explicit acceptance of the Terms of Service and Privacy Policy and pass acceptedTerms=true before calling this. Give the user both policy links and ask it as a multiple-choice question ("Accept Terms & Privacy" / "Decline").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobTokenYesThe jobToken returned by generate_logo.
variationIdYesThe variationId of the design to purchase, from generate_logo_variations.
acceptedTermsYesThe purchase is gated on Terms & Privacy consent. Before calling, give the user the Terms of Service and Privacy Policy links and ask them to accept as a multiple-choice question — "Accept Terms & Privacy" / "Decline". Pass true ONLY after the user picks accept; if they decline, stop and do not purchase. The checkout page will also require them to accept.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden. It discloses that this is a paid purchase, the pricing tier, the checkout-link behavior, the deliverable change after payment, and the mandatory Terms/Privacy acceptance gate. This gives the agent a clear picture of side effects and prerequisites.

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?

The description is front-loaded with the core purpose and then covers payment, retrieval, and consent. Every sentence adds necessary information, though 'This is a paid purchase' slightly repeats the pricing/checkout context already given. Still, it is appropriately sized for a high-stakes purchase tool.

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?

The description covers purpose, prerequisites, pricing, consent flow, and post-payment retrieval. It does not explicitly state what the function itself returns (e.g., whether the checkout link is the direct return value), but the overall guidance is sufficient for correct invocation despite no 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 adds meaningful context beyond the schema: jobToken comes from generate_logo, variationId comes from generate_logo_variations, and acceptedTerms requires a specific user consent interaction. This enriches all three parameters without redundancy.

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 states a specific verb ('Purchase') and resource ('alternate designs from generate_logo_variations'), and explicitly distinguishes it from buying the original logo. This clearly differentiates it from sibling tools like generate_logo and generate_logo_variations.

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?

It explains when to use the tool (after generating variations, instead of the original logo), how to retrieve the result afterward (poll get_logo_result with the same jobToken), and the required consent flow including what to do if the user declines. This is explicit, actionable guidance with no ambiguity.

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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TDQS

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: logo generation, logo variations, variation purchase, result polling, color palette, meta tags, schema markup, pricing, blog search, and health check. The two logo-generation tools are clearly differentiated by new-job vs existing-job workflows, so an agent should not confuse them.

Naming Consistency4/5

Most tools follow a clean verb_noun convention: generate_logo, generate_color_palette, get_logo_result, search_blog, buy_logo_variation. The lone echo and the singular/plural mismatch between buy_logo_variation and generate_logo_variations are minor deviations from an otherwise consistent pattern.

Tool Count5/5

With 10 tools, the server is well scoped. The paid logo workflow is covered by generate, vary, buy, and retrieve tools, while supporting tools for palettes, SEO metadata, pricing, and blog search each serve a distinct purpose without bloat.

Completeness4/5

The core logo lifecycle is well covered: generate, create variations, purchase a variation, and retrieve the paid deliverable. However, the descriptions advertise upgrades and revision credits but provide no explicit tool to purchase or apply them, leaving a minor gap that agents must work around.

Resources