WORKFLOW: 5-email campaign (subjects+bodies+CTAs+schedule). input=brief. B2B: growth teams launch nurture flows. [x402: 20.0 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?
The description discloses useful behavioral details such as the campaign components and a pay-per-use cost of 20.0 USDC on Base. However, with no annotations, it does not clarify whether emails are actually sent, how payment is triggered, or what the return format will be.
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 a single, front-loaded line that covers output, input, use case, and pricing in order of importance. Each phrase carries distinct information, and there is no filler.
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 one-input generative tool with no output schema, the description gives enough context: input brief, output components, target users, and cost. The main gaps are the exact return structure and payment prerequisites, but these are not critical for basic invocation decisions.
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 input as 'service input,' which is generic. The description adds meaning by specifying that the input should be a brief and that it feeds into subjects, bodies, CTAs, and scheduling. Still, the expected structure or length of the brief is not detailed.
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 deliverable: a 5-email campaign with subjects, bodies, CTAs, and a schedule, taking a brief as input. This makes it distinguishable from single-email sibling tools like email-polish or sales-email, though it lacks an explicit action verb like 'generates' or 'creates'.
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 intended context: B2B growth teams launching nurture flows. This helps an agent decide when this tool fits, but it does not provide explicit exclusions or name specific alternative tools for when not to use it.
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.