WORKFLOW: titles+hook+full script+SEO+thumbnail ideas. input=topic. B2B: creators/brands produce videos faster. [x402: 15.0 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | service input |
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?
No annotations exist, so the description carries the transparency burden. It does disclose the x402 pay-per-use model and the 15 USDC cost on Base, which is meaningful operational context. But it is silent on output format, persistence of data, and failure/refund behavior, leaving important behavioral unknowns.
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 compact and front-loaded with the workflow outputs before the target audience and pricing. Each fragment adds information, though the telegraphic style and all-caps format make it slightly less readable than a structured sentence.
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 definition covers the essential input (topic) and the expected deliverables, which is adequate for a one-parameter tool with no output schema. It is not complete enough about how the result is returned, the script length/style, or the practical behavior of a paid pipeline beyond the upfront cost.
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?
The schema only describes the parameter as 'service input', while the description clarifies that the input is a 'topic'—a real semantic improvement. With only one parameter, this is sufficient, though format and examples are still missing.
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 lists specific deliverables—'titles+hook+full script+SEO+thumbnail ideas'—and states the input is a topic, so an agent can infer this is a YouTube script generation workflow. It lacks an explicit verb like 'generates', but it is clearly distinguishable from sibling pipelines such as blog-post-pipeline or podcast-pipeline.
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?
It gives a clear use context: 'B2B: creators/brands produce videos faster' and defines the required input as a topic. However, it does not name alternative tools or state when not to use it, so the agent is not given explicit switching guidance.
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.
The set contains many trivially indistinct tools: ai-inference/inference, compress/comprimir, count-tokens/contar-tokens, detect-language/language-detect, and multiple overlapping OCR receipt variants. With 160 tools and pairs that differ only by language or suffix, an agent cannot reliably distinguish several capabilities.
Most names are readable lower-hyphen identifiers, but they mix action verbs, noun phrases, domain prefixes, pipeline suffixes, Spanish/English, and arbitrary demo/batch labels. There is a loose convention, but no consistent verb_noun pattern.
160 tools on one server is an extreme count and clearly unwieldy. Even as a marketplace, exposing every variant, demo, and composed bundle as a top-level MCP tool overwhelms agent selection and adds little distinct capability.
The set covers a huge range of text, image, audio, code, market, compliance, and content-workflow tasks, so many intents have some available tool. However, it is a grab-bag rather than a defined service surface, and the arbitrary demo/specialized variants make it unclear whether a needed operation truly exists or is just a duplicate.