WORKFLOW: research+headlines+article+SEO meta in one call. input=topic. B2B: content teams ship a publish-ready post. [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?
With no annotations, the description carries the disclosure burden and does add useful behavioral facts: it is a one-call multi-stage workflow, produces a publish-ready post, and is pay-per-use at 15.0 USDC on Base. However, it omits the output structure, payment flow, and any limitations, so transparency is only partial.
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 with no wasted words: the workflow is front-loaded, followed by the target user, output promise, and pricing. Every segment earns its place.
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 invocation facts for a one-parameter tool: input topic, pipeline components, target user, and cost. But with no output schema, it leaves an agent unsure about the exact return shape (single article versus separate SEO meta fields) and how x402 payment is handled, so it is adequate with clear gaps.
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 parameter description is a generic placeholder ('service input'), so the tool's 'input=topic' is what actually tells the agent what to pass. This is meaningful added semantics, though it could specify topic form or length.
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 the tool assembles research, headlines, article, and SEO meta into a publish-ready blog post from a topic in one call. This clearly identifies the resource and workflow, and the combination of stages distinguishes it from single-purpose siblings such as headlines or seo-article, though it never names an alternative explicitly.
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?
"B2B: content teams ship a publish-ready post" gives a clear target audience and use case, and the workflow label signals when the full pipeline is wanted. It does not list exclusions or explicitly compare to content-pipeline, but the context is strong enough to guide selection.
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.