F1 race-weekend field scan — winner/podium/top-N/H2H/constructor/quali (also via signal/sports?sport=f1) [PAID — signal credit or x402 USDC. Cost: 1 signal credit ($1.70-$2.49/credit by pack size). Uncredentialed calls return the 402 payment envelope; set X-API-KEY on the MCP connection or pay x402 out-of-band at GET /api/signal/f1.]
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It meaningfully discloses the paid nature, exact cost, the 402 payment envelope for uncredentialed calls, and the two auth paths (X-API-KEY or x402). It does not describe response shape or rate limits, but for a paid read-style scanner, the payment/auth behavior is the most important context.
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 one dense, front-loaded sentence, with the key purpose stated first and the critical billing/auth details in a clear parenthetical. It is not wasteful, but the pricing/payment chain is bundled into a long parenthetical that could be slightly better structured.
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 zero-parameter tool with no output schema, the description is unusually complete: it explains what data is covered, how to access the same data via an alternate route, payment requirements, cost, failure behavior, and authentication setup. It also delegates parameter documentation to the instruments tool rather than leaving the caller guessing.
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 tool has zero defined parameters, so the baseline is 4; the description goes beyond that by naming the semantic field categories (winner, podium, top-N, H2H, constructor, quali) that a caller would likely pass as query parameters. It also points to the instruments tool for per-endpoint parameter docs, which helps compensate for the open-ended additionalProperties schema.
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 uses a specific verb phrase 'F1 race-weekend field scan' and enumerates the exact data categories: winner/podium/top-N/H2H/constructor/quali. It clearly distinguishes this from the many sibling scan/signal tools by naming the sport and noting the alternative route via signal/sports?sport=f1.
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 description makes clear when to use the tool: for F1 race-weekend field scans, and it additionally names the alternate endpoint via signal/sports. It does not explicitly list when not to use it or name alternatives like other sport-specific signal tools, so it gets a 4 rather than a 5.
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.
There is significant overlap between scan_* and signal_* tools for the same underlying asset classes, e.g. scan_futures vs signal_futures, scan_racing vs signal_racing, and scan_predmarket vs signal_polymarket. Broader catch-alls like analysis, scan_ask, backtest, and signal_generate also blur the boundary, forcing an agent to parse long pricing details before knowing which tool actually applies.
The overwhelming majority of tools follow a clear `scan_` or `signal_` snake_case prefix, which makes the product families easy to recognize. A small set of standalone unprefixed tools — analysis, backtest, instruments, leaderboard, quote, track_record — breaks the pattern, but the overall scheme is still consistent enough to infer.
47 tools is far beyond the practical range for an agent to reason about, even though the server's domain is broad and heavily segmented. Many specialist endpoints could be consolidated under fewer catch-all scanners and signals, but the exposed surface instead forces a large tool-selection decision on every request.
The tool surface covers discovery, cost preview, sample analysis, public track records, leaderboards, broad market scanning, asset-class-specific scanning, sports and event signals, and prediction-market verticals. There are minor gaps in explicit account/credit management and some redundant paths, but for a signal/research service the workflow is largely complete.