$0.09 via x402: live macro/economic indicators for any of 200+ countries in one call — GDP, GDP growth %, inflation (CPI), unemployment %, GDP per capita, population, with the year of each. Official public-domain World Bank data. For macro, finance, research and trading agents.
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes | ISO2 or ISO3 country code, e.g. USA, DE, JPN, CN, GBR, IND, BRA | |
| x_payment | No |
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 transparency burden. It adds useful behavioral context: the $0.09 cost via x402, the World Bank public-domain source, live indicator data, and the inclusion of the year for each value. However, it does not disclose potential failure modes, response format, or details about payment handling, leaving some important operational ambiguity.
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 a single, dense sentence that packs in cost, scope, indicators, data vintage, source, and target audience. Every clause contributes information, and the use of em-dashes and lists keeps it readable. It is slightly long but appropriately so for the number of indicators and context it conveys.
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?
Without an output schema, the description compensates by listing the exact indicators returned and noting that each includes its year, giving an agent a solid mental model of the response. Combined with the source and cost information, this covers most of the essential context, though it omits the response format and any x_payment mechanics. The tool is simple enough that this is nearly complete.
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 coverage is 50%: 'country' is described with ISO2/ISO3 examples, but 'x_payment' has no description. The tool description mentions the $0.09 x402 cost but does not explain how the 'x_payment' parameter should be used or what value it expects. It also adds useful context for 'country' by stating 200+ countries, but the critical payment parameter remains underspecified.
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 resource (macro/economic indicators for 200+ countries) and enumerates the specific indicators (GDP, inflation, unemployment, etc.), which effectively distinguishes it from similar macroeconomic tools like us_macro_regime. However, it lacks an explicit imperative verb (e.g., 'retrieve' or 'get'), relying on the tool's noun name to convey the action.
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 phrase 'For macro, finance, research and trading agents' gives a target audience and the 'in one call' efficiency suggests a broad lookup use case, but it does not explicitly state when to choose this tool over alternatives or when not to use it. No sibling tools are named, and no exclusions are provided.
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 occupy the same conceptual space: web_scrape vs markdown_web_scraper, post_check vs brand_ai_visibility_check, llm_chat_completions vs post_api_v1_chat_completions, chain_transaction_status vs chain_confirmations, and connect_token vs token_security_check + dex_token_data. Descriptions help in places, but for an agent facing 92 tools these near-overlapping endpoints will frequently cause misselection.
Everything is snake_case, but the conventions diverge sharply: get_chain_* and chain_* coexist for the same RPC family, post_* names are HTTP-route artifacts, api_generate reverses noun_verb order, and many names are bare nouns rather than verb_noun. There is no predictable naming pattern an agent can rely on.
At 92 tools this is far beyond the range where an agent can keep the surface coherent, even for a store. The flat tool list mixes products, bundles, aliases, proxies and single-use verticals, so most of the count is noise for any given task. A catalog/search/payment model with fewer exposed tools would fit the storefront purpose better.
The server has impressive breadth and covers key storefront/market workflows: catalog, samples, credits, directory listing, notary, and the task lifecycle. But each domain is shallow: there is no chain transaction broadcast, no task update/cancel/dispute, no AI-visibility history, and many verticals are a single tool with no follow-on operation. The surface is broad but not deeply complete.