Thematic-basket / sector-ETF signal [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/basket.]
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are not provided, so the description carries the burden. It discloses payment requirements and the 402 envelope behavior, which is a key behavioral trait. It does not describe response format or what happens after payment, but the payment-related behavior is a valuable disclosure. For a paid tool, this is helpful but not exhaustive.
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 a single paragraph with dense information about payment, cost, and error handling. It is front-loaded with the tool's purpose and payment requirement. Slightly long but every sentence adds context about payment and authentication. No wasted words, but could be split into bullet points for readability, though it's acceptable.
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?
This is a paid API with 0 parameters and no output schema. The description provides critical payment and error-handling context, which is essential. However, it does not explain what a successful response looks like or how the signal is structured, which could be inferred from 'signal' but is not explicit. Given the tool's simplicity (no params, no output schema), the description is largely complete but could benefit from a note on what to expect on success.
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?
Schema coverage is 100% but there are 0 parameters. The description references the 'instruments' tool for parameter docs via additionalProperties, which effectively explains that parameters are allowed but undocumented here. This is a reasonable delegation since the schema itself is unclear but the description provides a path to discover parameters. It adds value beyond the schema by pointing to the instruments tool.
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 identifies the tool as 'Thematic-basket / sector-ETF signal' and mentions it is a paid signal. It is distinct from siblings like signal_equities or signal_ticker by specifying thematic-basket/sector-ETF focus. It does not explicitly say 'get' or 'retrieve', but the intent is clear.
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 payment requirement and how to handle uncredentialed calls (returns 402 envelope, set X-API-KEY or pay x402 out-of-band). It does not explicitly contrast with siblings, but the payment context and endpoint reference provide meaningful usage guidance. It could be stronger with a direct 'use this when...' but the context is clear.
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.