WORKFLOW: episode title+structure+guest questions+show notes. input=topic. B2B: podcast studios pre-produce episodes. [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?
There are no annotations, so the description carries full behavioral disclosure responsibility. It does usefully disclose the pay-per-use model and exact cost (15.0 USDC on Base) plus the output artifacts. However, it does not describe side effects, failure behavior, processing characteristics, or any external calls, leaving meaningful gaps.
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, leading with the workflow outputs before input and pricing. Each fragment carries distinct information with no filler. The telegraphic style is slightly terse but still efficient.
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?
With only one parameter and no output schema, the description provides the key operational facts: input type, output artifacts, target audience, and pricing. It lacks an example topic or an explicit statement of return structure, but the listed deliverables largely compensate for the missing output schema.
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's parameter description is just 'service input', which is uninformative. The description adds meaning by stating the expected input is a topic and connecting that input to the produced episode deliverables, giving an agent enough semantic grounding to invoke it correctly.
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 identifies a concrete workflow and lists specific artifacts—episode title, structure, guest questions, and show notes—so an agent can infer that this tool produces podcast pre-production materials from a topic. The 'podcast' naming and B2B positioning help distinguish it from generic content pipelines. It lacks an explicit verb form but the resource and output are clear.
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 line 'B2B: podcast studios pre-produce episodes' gives a clear target scenario, and 'input=topic' communicates the minimal call setup. It does not name sibling alternatives or provide when-not-to-use guidance, but the context is specific enough for an agent to select this tool confidently.
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.