x402_x402tap_com_api_weather_0[PAID inbound $0.0011 → then proxy $0.0011] Live current weather conditions for any city, sourced from Open-Meteo Upstream: https://x402tap.com/api/weather Method: GET Example query: {"city":"Austin"}
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | Optional JSON body for non-GET requests | |
| query | No | Optional query string parameters for the upstream HTTP request |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden but only states the data source, method, and an example query. It omits the response format, error behavior, rate limits, and what 'PAID inbound' actually means operationally.
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 concise and front-loaded with the core action, followed by source, method, and example. The pricing bracket is terse and not overly verbose, so the overall structure is clean.
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?
Given the large set of weather-related sibling tools, the description does not differentiate this tool or indicate when it is the best choice. There is no output schema and no mention of return units, location formats, or failure modes, leaving the context incomplete.
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 input schema only exposes generic body and query objects with no named properties. The description adds an example city query, which helps slightly, but the body parameter and any other accepted query fields remain completely unexplained.
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?
Description clearly states it retrieves live current weather for any city from Open-Meteo via a GET endpoint and includes an example query, making the primary purpose evident. It does not explicitly name sibling alternatives, but the action is unambiguous.
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?
No guidance is provided on when to use this tool instead of the many sibling weather tools, or how the paid proxy flow affects usage. The 'PAID' marker is not explained as a usage condition.
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 core dispatcher tools are distinct, but the 84 auto-discovered wrappers contain dozens of near-identical news, weather, and translation endpoints. An agent cannot reliably tell many of these apart without reading provider-specific details, and misselection is likely.
Core tools use a readable verb_noun pattern, but most tools are machine-generated `x402_<domain>_<path>_<number>` names with opaque numeric suffixes. The two naming schemes are mixed, and the generated names are not intuitive.
95 tools is an extreme mismatch for a coherent MCP surface. The bulk are auto-registered third-party proxies that duplicate the same few categories, creating massive prompt bloat and selection overhead.
For an x402 dispatcher, the core lifecycle is well covered: discovery, search, quote, route-and-call, stats, paywall status, and cashflow/P&L. Minor gaps such as no deregistration or detailed per-transaction drill-down exist, but main workflows do not dead-end.