Tokenized-stock / pre-IPO-perp jurisdiction-eligibility check [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/tokenized-eligibility.]
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It transparently discloses that the tool is paid, details the cost, and explains the payment/authentication flow (X-API-KEY or x402), including the 402 response for uncredentialed calls. However, it does not describe the actual success response or the nature of the eligibility check beyond the name, leaving a gap in behavioral expectations.
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 but informative, with the purpose front-loaded and payment details bracketed. Every sentence adds value: purpose, cost, and authentication method. It is not overly verbose and avoids redundancy.
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 it has no output schema and no annotations, the description explains the payment and access mechanics but does not describe what the eligibility check returns (e.g., a boolean, reasons, or status codes). This is a notable gap for a tool with no other structured metadata, making it incomplete for full usage understanding.
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 has 0 parameters but allows arbitrary additional properties, with schema coverage at 100% due to the explanation. The description (via the schema) points to the free 'instruments' tool for parameter documentation, which compensates for the lack of specific parameter info. Since there are 0 formal parameters, baseline is 4, and this pointer keeps it at that level.
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 performs a jurisdiction-eligibility check for tokenized stocks and pre-IPO perps, using a specific verb ('check') and resource ('jurisdiction-eligibility'). This distinguishes it from sibling tools like signal_tokenized or signal_pre_ipo, which focus on data or signals rather than eligibility.
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 provides clear context on when to use the tool (for eligibility checks) and includes payment and authentication requirements. However, it does not explicitly state when not to use it or name alternative tools for similar functions, though the schema pointer to 'instruments' for parameter docs adds some guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.