Generate AI logo concepts (icon + palette + typography + layout) for a business. Returns N variations. Requires Founder sub or one-time LOGO_PACK/BRAND_KIT purchase. [Requires a free namemy.app API key — get one at https://namemy.app/app/api-keys]
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| count | No | Number of variations (1-9) | |
| slogan | No | ||
| description | Yes | What the business does | |
| preferences | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the AI-generated nature, output variations ('Returns N variations'), and required access conditions. It does not cover rate limits or error handling, but the core behavioral traits are well communicated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences that front-load the core action in the first sentence and then provide essential prerequisites and API key information in the second. No fluff or redundant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core function and prerequisites but lacks details on the output format (e.g., how variations are returned) and does not fully explain the nested preferences object. With no output schema and low schema coverage, more detail would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 40%. The description adds context by mentioning palette and typography, which relate to the preferences object, and confirms the count parameter via 'N variations'. However, it does not explicitly explain parameters like slogan or name, relying on self-explanatory names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates AI logo concepts with specific components (icon, palette, typography, layout) for a business. It distinguishes from sibling tools like generate_brand_kit and generate_names by focusing on logo variations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides prerequisites (Founder sub, one-time purchase, free API key) but does not explicitly compare to alternatives like generate_brand_kit or generate_social_kit. Usage is implied by the tool's name and description rather than explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes (generation, checking, buying, DNS). The main overlap is between check_domain and check_domain_public, but descriptions clearly favor the public version, reducing ambiguity. buy_domain and buy_link are also distinct (direct purchase vs. link generation).
The majority follow a verb_noun pattern (buy_domain, generate_logo, list_domains). A few deviations exist: brand_conflict_check reverses the order, and the check_domain variants have inconsistent singular/plural forms. Overall, the pattern is still easily recognizable.
13 tools covers the domain from name generation, conflict checking, domain purchase, DNS management, and brand asset generation without being overwhelming. Each tool serves a logical part of the workflow, and the count is within the ideal 3-15 range.
The core domain lifecycle is covered: generate names, check/buy domains, list owned domains, and set DNS. Brand assets (logo, legal, social, brand kit) are also covered. Minor gaps like a delete domain or DNS delete tool exist, but they are not critical for the primary workflow.