get_mbta_predictionsPAID inbound $0.01, then V1 demo: settle $0.01 USDC Base via treasury, then return live MBTA predictions.
| Name | Required | Description | Default |
|---|---|---|---|
| stop_id | Yes | MBTA stop ID, for example place-pktrm or 70065 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and a description centered on payment flow, the tool's actual behavior (e.g., return format, errors, rate limits) is not disclosed. The payment mention is confusing and does not clarify the prediction behavior.
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 in length but poorly structured, mixing payment instructions with the tool's purpose. It is not a clear, well-organized sentence but a run-on that obscures the main function.
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?
The description lacks essential context about MBTA predictions, such as what data is returned, how to interpret results, or any prerequisites. It is incomplete and leaves an agent without sufficient information to decide on usage.
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 provides a clear description for the single parameter `stop_id` with examples (`place-pktrm` or `70065`), which explains its meaning and format well. The tool description adds no further detail, but the schema itself is strong.
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 focuses on payment and settlement details rather than stating what the tool does. It mentions 'return live MBTA predictions' but buries it under payment instructions, making the purpose unclear and misleading.
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 versus alternatives. The description does not indicate appropriate contexts, filtering scenarios, or distinctions from sibling tools.
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.