Generate a JSON-LD block for a schema.org type (LocalBusiness, Organization, WebSite, Article, FAQPage, or Product) from a flat data object. For FAQPage, pass data.items as [{ question, answer }].
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | Fields for the chosen type, e.g. { name, url, description, telephone }. FAQPage uses { items: [{ question, answer }] }. | |
| type | Yes | schema.org type to generate. |
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 behavioral burden. It clearly communicates that the tool transforms a flat data object into markup and highlights the FAQPage items format. But it does not explicitly state that the operation is side-effect-free or describe validation/error behavior, leaving some ambiguity.
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, front-loads the main purpose, and uses its second sentence only for a valuable edge case. Every word earns its place; there is no fluff or redundant restatement.
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?
For a simple generation tool with a rich input schema, the description covers the main call path and the FAQPage exception. It tells the agent what will be produced ('JSON-LD <script> block') and the key input shape. It could slightly improve by noting that the output is returned as a string, but that is largely inferable.
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 description coverage is 100%, so the schema already documents both the 'type' enum and the 'data' object shape. The FAQPage note in the description essentially repeats what the schema already says, adding no meaningful parameter semantics beyond the structured schema.
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 states a specific verb ('Generate'), a clear resource ('JSON-LD <script> block for a schema.org type'), and even enumerates supported types. This makes it immediately distinguishable from sibling tools like generate_meta_tags, which handle HTML meta tags rather than structured data markup.
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 implies when to use the tool: when JSON-LD schema markup is needed for one of the listed schema.org types. However, it provides no explicit when-not-to-use guidance or alternatives, and the only conditional note is the FAQPage input shape.
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.
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.
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.
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.
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.