$0.09 via x402: Global Weather Intelligence — global weather forecasts using open meteorological data.
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude coordinate | |
| longitude | Yes | Longitude coordinate | |
| x_payment | No | Optional signed x402 payment payload |
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 must carry behavioral context; it does add the facts that data comes from open meteorological sources and that the call costs $0.09 via x402, which hints at payment/auth behavior. It does not explain what the response contains, whether caching or rate limits apply, or how payment payloads are handled.
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 at one sentence, but it leads with price and a 'Global Weather Intelligence' tagline before the actual purpose, making the structure less useful for an agent. The repeated 'Global' and the marketing-style label add minor noise.
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 tool with no output schema and no annotations, this is thin: an agent cannot tell what weather variables, units, time horizons, or result format to expect. The optional x_payment field and the $0.09 cost also leave the payment flow ambiguous.
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%, so latitude and longitude are already documented; the description's global focus confirms but does not extend their semantics. It adds no detail on coordinate format or how the optional x402 payment parameter relates to the stated cost, leaving the baseline at 3.
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 producing global weather forecasts from open meteorological data, which distinguishes it from the blockchain, search, and market siblings. It lacks an explicit verb phrase such as 'retrieves' or 'returns', but the resource and scope are clear enough.
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 'global weather forecasts' implies the tool is for weather lookups by latitude and longitude, and no sibling tool is an obvious weather alternative. However, the description does not explicitly state when to choose this over other tools or mention any prerequisites or exclusions.
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.