ecommerce-pricing
VERTICAL(ecommerce): dynamic pricing strategy + rules. input=catalog+goal. B2B: online stores optimize margin/volume. [x402: 15.0 USDC on Base, pay-per-use]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | service input |
VERTICAL(ecommerce): dynamic pricing strategy + rules. input=catalog+goal. B2B: online stores optimize margin/volume. [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 are present, so the description bears the full burden of behavioral disclosure. It mentions that the tool accepts a catalog and goal and produces pricing strategy/rules, but it does not describe the output format, whether it returns a document or rules, any prerequisites, or side effects. The payment note about x402 is operational, not behavioral.
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 telegraphic and packs vertical, purpose, input, use case, and pricing into a few fragments. Every segment adds information, though the internal codes 'VERTICAL' and 'x402' may be cryptic to an agent. It is concise rather than overly verbose.
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 single-parameter tool with no output schema or annotations, the description gives the core idea, input semantics, and target audience. However, it omits the expected output shape and gives no example input. It is adequate but leaves an agent uncertain about what the returned pricing strategy/rules will look like.
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 provides the generic parameter description 'service input', but the tool description clarifies that the input should contain a catalog and a goal. This adds meaningful semantics beyond the schema and helps an agent construct the input correctly, even though it does not specify the exact format.
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 the tool as an ecommerce vertical tool providing 'dynamic pricing strategy + rules' and states the goal of optimizing margin/volume for online stores. While there is no explicit imperative verb like 'generate' or 'create', the resource and outcome are clear. The 'VERTICAL(ecommerce)' tag differentiates it from related pricing siblings such as global-saas-pricing.
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 context: use this for B2B online stores seeking to optimize margin or volume. It implies a domain-specific use case but does not explicitly list exclusions or alternative tools. The context is specific enough to guide an agent, though it could be improved by explicitly naming the SaaS pricing sibling as the alternative.
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.