FX scanner, 28 pairs, 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/forex.]
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full weight and provides substantial details: the tool is paid, costs 1 credit or x402 USDC, unauthenticated calls return a 402, and API key setup is explained. This goes beyond minimal transparency, though it doesn't cover every behavioral aspect like error handling or response format.
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 uses a concise lead-in ('FX scanner, 28 pairs') followed by bracketed payment/auth details, keeping the most relevant info first. While a bit long, the structure is logical and each part adds necessary context.
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?
The tool is relatively simple (no schema-defined parameters, no output schema), and the description covers payment, auth, and a key parameter. However, it lacks any note on the response shape or typical usage example, leaving room for minor uncertainty.
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 parameters (additionalProperties allowed), and the description introduces the 'style' parameter with allowed values, adding value over the schema. It also directs to the instruments tool for parameter docs. Yet, it doesn't list all possible query params, so it's not comprehensive.
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 'FX scanner, 28 pairs' and lists a 'style' parameter with possible values, indicating the tool scans forex pairs. However, it doesn't specify the output nature (e.g., signals, opportunities), so it stops short of a 5.
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?
Usage is implied through naming ('forex') and the mention of 'call the free instruments tool for per-endpoint parameter docs' provides some guidance. However, it doesn't explicitly differentiate from sibling scan_ tools or state when to prefer this tool, so guidance is only partially explicit.
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.