Compare countries (bundle) — One call: two countries' key economics side by side — GDP, GDP per capita, GDP growth, inflation and population, each with which country's figure is higher. Source: World Bank Open Data. Pass two ISO country codes. Price: $0.04 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | first ISO country code, e.g. US | |
| b | Yes | second ISO country code, e.g. CN |
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?
Annotations already indicate read-only and non-destructive behavior. The description adds valuable behavioral context: it names the data source (World Bank Open Data), discloses the price, and clarifies that the output includes which country's figure is higher for each metric. This goes beyond what annotations provide, though it does not describe output formatting or edge cases.
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 with the core purpose, followed by specific metrics, source, usage requirement, and price. Every element adds useful information for an agent deciding whether to call this tool.
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 simple comparison tool with two fully documented parameters and no output schema, the description adequately explains the input, the metrics returned, and the comparison behavior. It could be slightly more complete by mentioning output format or data-year caveats, but it is sufficient for correct selection and invocation.
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?
Schema coverage is 100%, with both parameters already fully described as ISO country codes with examples. The description merely restates 'pass two ISO country codes' and adds no deeper parameter semantics, so the schema is doing the heavy lifting.
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 a specific verb and resource: compare two countries' key economics in one call, listing the exact metrics (GDP, GDP per capita, GDP growth, inflation, population) and the comparison output. It clearly differentiates itself from single-country or data-lookup siblings like econ_country_snapshot and econ_worldbank.
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 use case is implied strongly: call this tool when a side-by-side comparison of two countries' economic indicators is needed. However, it does not explicitly state when not to use it or mention alternatives such as econ_country_snapshot for a single country or econ_worldbank for broader datasets.
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.
Many tools are clearly separated by prefix and data source, but several bundled products overlap heavily: vehicle_deal_check vs vehicle_report, realestate_property_report vs realestate_site_risk, finance_company_360 vs finance_health_scan, and domain_due_diligence vs email_domain_check/business_vet. An agent would frequently struggle to pick the correct premium bundle.
Tool names overwhelmingly follow a consistent snake_case category-prefix pattern like weather_, crypto_, vehicle_, finance_, and geo_. Minor deviations such as bare names (domain, ip) and noun-verb forms (dns_lookup, url_check) are easy to learn and don't create real confusion.
50 tools is far beyond the typical well-scoped 3–15 range and will require heavy filtering to navigate. The broad multi-domain data marketplace partially justifies the size, but it would be more coherent split into per-domain servers or consolidated further.
For a read-only data/diligence marketplace, the surface is quite comprehensive: weather, vehicle, crypto, SEC/finance, domain/email, sanctions, and geo workflows all have core operations plus fused verdict bundles. Minor gaps exist—such as a simple crypto price lookup or vehicle market value—but agents can usually work around them.