Cross-asset market scanner (42 instruments: 28 FX + metals/indices/crypto CFDs), style=scalp|intraday|longterm [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/market.]
| Name | Required | Description | Default |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description adds crucial behavioral details: it is a paid tool requiring signal credits or x402 USDC, and uncredentialed calls return a 402 payment envelope. It also instructs on authentication (X-API-KEY) and out-of-band payment, exceeding what annotations would typically provide.
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, information-rich sentence that front-loads the core purpose and includes necessary payment/auth details in brackets. While longer than minimal examples, every component adds value and it is well-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?
Given the tool's nature as a GET scanner with open query parameters, the description covers asset scope, style parameter, cost, and authentication. It does not specify output format, but no output schema exists, and the schema description points to the 'instruments' tool for further parameter docs, which is a helpful pointer.
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 is open (additionalProperties string) with no defined params, but the description introduces the 'style' parameter and its allowed values (scalp|intraday|longterm). This adds meaning beyond the empty schema, though it does not elaborate on other potential query parameters.
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 the market across 42 instruments (FX, metals, indices, crypto CFDs) and supports a style parameter (scalp, intraday, longterm). This distinguishes it from siblings like scan_forex or scan_crypto by explicitly being cross-asset.
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 implies usage context: use when a cross-asset scan is needed, as it covers multiple instrument types. It does not explicitly mention alternatives or exclusions, but the cross-asset nature is a clear differentiator from asset-specific siblings.
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.