Poll a logo job by its jobToken. Once payment has settled, returns a URL to download the full-resolution, watermark-free asset; otherwise a pending status. Read-only and safe to poll — after the user pays, keep polling every ~5 seconds for up to a few minutes (payment confirmation is not instant) until the status is paid.
| Name | Required | Description | Default |
|---|---|---|---|
| jobToken | Yes | The jobToken returned by generate_logo. |
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 provided, the description carries the full behavioral burden and does well: declares the operation read-only and safe to poll, explains pending vs paid statuses, and discloses that payment confirmation is not instant. It does not cover failure or timeout behavior, but that is not essential for basic use.
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?
Two sentences, front-loaded with the action and resource, then the return behavior and polling guidance. Every clause adds useful information; no filler or repetition of schema details.
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 tool is simple with one required parameter, and despite lacking an output schema or annotations, the description covers input provenance, return behavior, and polling cadence. It would be complete for an agent to call it correctly; the only omission is handling of non-paid/non-pending terminal states.
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 100% and the only parameter, jobToken, is already described in the schema as the token returned by generate_logo. The description adds little beyond restating that the job is polled by jobToken, so baseline 3 is appropriate.
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?
States a specific verb ('Poll'), a resource ('a logo job'), and an identifier ('jobToken'), then explains the two outcomes (paid URL vs pending). This clearly distinguishes it from sibling generation tools like generate_logo and buy_logo_variation.
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?
Gives explicit usage context: after payment, poll every ~5 seconds for up to a few minutes until status is paid. It doesn't name alternative tools, but no sibling is a polling alternative, so the lack of an explicit when-not is a minor gap.
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.