Direct fantasy advice — start/sit, DFS lineup (contest=gpp|cash, slate override, book=dk), waivers, trades [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/scan/fantasy.]
| 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 burden and does substantial work: it discloses that calls are paid, costs are variable by pack size, uncredentialed calls receive a 402 payment envelope, and authentication can be done via X-API-KEY or out-of-band x402. It does not cover rate limits or response shape, but the payment/auth/failure behavior is well disclosed.
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?
Front-loaded with the core purpose and efficient with every clause serving a purpose. The payment/auth details are packed into one long parenthetical sentence, making it somewhat dense, but there is no wasted filler.
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 paid tool with no annotations and no output schema, the description covers the essential context: what the tool does, how to pay, how to authenticate, what happens on failure, and where to find parameter documentation. It does not describe the return shape or rate limits, but it is reasonably complete for the tool's apparent complexity.
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 has zero named parameters, so the baseline is 4. The description adds useful parameter hints such as contest=gpp|cash, slate override, and book=dk, and directs users to the instruments tool for per-endpoint parameter docs, providing more insight than the otherwise empty 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?
States a specific action ('Direct fantasy advice') on a specific resource ('fantasy'), and lists concrete subdomains: start/sit, DFS lineup, waivers, trades. This clearly distinguishes it from sibling market-focused scan tools like scan_crypto, scan_forex, and scan_golf.
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?
Provides clear usage context (fantasy advice domain) and operational prerequisites such as payment, X-API-KEY setup, and the pointer to the free instruments tool for parameter docs. However, it does not explicitly state when to choose this tool over alternatives or when not to use it.
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.