Futures scanner by category (index/energy/metals/ags/rates) [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/futures.]
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that uncredentialed calls return a 402 payment envelope, which is a key behavioral trait. It also explains the payment flow (signal credit or x402 USDC) and the location of the API endpoint, exceeding the baseline by explicitly warning about potential failed calls and cost implications.
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 somewhat long, wrapping key payment details inline with behavior. While all information is useful, the block of bracket-enclosed text is dense. It could be broken down for readability, but it remains directly relevant and non-repetitive.
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 tool's complexity (0 defined parameters, no output schema), the description covers critical context: cost, auth failure behavior, and pointer for parameter docs. It doesn't explain return value structure, but with no output schema and permissive params, the description is fairly complete for an agent, though it could hint at the response shape.
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 parameter schema is permissive (additionalProperties: string) with no defined properties. The description references GET /api/scan/futures and points to an "instruments" tool for parameter docs, which is the only way to learn about parameters. Since no parameters are defined in the schema, the description's pointer to external documentation is the only semantic signal, compensating for the schema's lack of detail.
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 states the tool scans futures by category and lists the specific categories (index/energy/metals/ags/rates). It differentiates itself from siblings by focusing exclusively on futures scan, which is distinct from the many other scan_ and signal_ tools.
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 explicitly states the paid nature, the cost, and payment methods. It also explains the behavior for uncredentialed calls (returns 402 payment envelope) and instructs how to authenticate (set X-API-KEY or pay x402), which serves as essential when-to-use guidance for the agent.
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.