INDUSTRY REPORT: Benchmarks globales de pricing SaaS (deep, cited). input=optional scope. B2B: equipos de producto/monetización calibran su pricing. [x402: 150.0 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 full burden. It discloses that the tool produces a cited, deep report and mentions a pay-per-use side effect ('x402: 150.0 USDC on Base'), which is useful. However, it does not describe important execution behavior beyond output, and the phrase 'input=optional scope' conflicts with the schema's required parameter, creating potential confusion.
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 very compact and front-loaded with the key purpose ('INDUSTRY REPORT'), followed by input, audience, and cost. It contains no filler, though the compressed notation like 'x402: 150.0 USDC on Base' is somewhat cryptic.
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?
Given the tool's simplicity, the description covers the core purpose, target audience, and usage cost. However, it is incomplete about what the input scope should be, what the report output structure looks like, and how this tool differs from neighboring report-generation siblings.
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 description for 'input' is generic ('service input'), so the description's 'input=optional scope' adds some meaning by suggesting the input is a scope filter. However, it is vague about what a valid scope looks like and 'optional' contradicts the required flag in the schema.
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 identifies the tool as generating an industry report of global SaaS pricing benchmarks, described as 'deep, cited.' It is specific about the resource (global SaaS pricing) and the report nature, though it does not explicitly distinguish itself from similar siblings like ecommerce-pricing or deep-research-report.
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 description gives clear usage context: B2B product and monetization teams calibrating their pricing. It implies when this tool is relevant, but does not explicitly name alternatives or state 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.