VERTICAL(retail): competitor/pricing/assortment intelligence. input=retailer+region. B2B: retailers tune assortment and pricing with fresh data. [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?
With no annotations, the description carries the burden of behavioral disclosure. It does reveal pay-per-use cost (20.0 USDC on Base) and frames the output as fresh data for retail pricing/assortment decisions. However, it does not disclose whether the call is read-only, how results are returned, or any rate/usage constraints beyond cost.
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: vertical, function, input, audience, and cost are all packed into a few short segments. Every clause carries useful information, and there is no filler or repetition.
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, the description covers the main invocation inputs, the audience, and pricing. However, it leaves unspecified details such as the exact expected format of 'retailer+region' and the structure of the returned intelligence, which an agent may need to safely handle results.
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 a generic 'service input' description, so the schema itself gives almost no semantic guidance. The description compensates by stating 'input=retailer+region', which is the key information an agent needs to construct the parameter. It could be more explicit about exact formatting, but it adds real meaning beyond 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 a retail vertical and states the tool's deliverable: competitor/pricing/assortment intelligence from retailer and region inputs. It is specific enough to understand the domain and value, though it lacks an explicit verb phrase and does not directly contrast with sibling tools like competitor-monitoring or competitive-analysis.
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 usage context: B2B retailers use it to tune assortment and pricing with fresh data. This implies when it should be used, but it does not explicitly state when not to use it or which alternative tools are more appropriate for broader/general competitive analysis.
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.