CMI scanner — 13 commodities/metals/indices CFDs scanned as one group (session-eligibility-aware), 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/cmi.]
| 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 carries the full burden and does well: it discloses that the tool is paid (1 signal credit or x402 USDC), specifies the cost range, explains the 402 payment envelope for uncredentialed calls, and describes how to configure X-API-KEY or pay out-of-band. It also mentions session-eligibility awareness and the style parameter, adding valuable behavioral context.
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 front-loads the core purpose in one dense clause, then compacts payment and authentication details into a bracketed tail. Every piece of information is relevant and there is no fluff, though the single long sentence could be slightly better structured for parseability.
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 moderately complex: paid, requires credentials, supports multiple styles, and scans a specific asset group. The description covers purpose, asset scope, style options, payment costs, credential methods, and the exact endpoint. The only notable gap is the absence of output/return format details, but with no output schema and a pointer to docs, the definition is largely complete for selecting and invoking the tool.
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 defined properties, so the description and schema-level hint to call the 'instruments' tool are the only parameter guidance. The main description adds the 'style=scalp|intraday|longterm' parameter with its allowed values and points to free per-endpoint docs. With zero declared parameters, the baseline is 4, and the description adds meaningful value beyond the empty schema.
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 explicitly identifies a scanner for 13 commodities/metals/indices CFDs grouped together, with a style parameter. This clearly distinguishes it from sibling scanner tools like scan_crypto, scan_forex, and scan_futures by naming the exact asset class and scope.
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 asset-class scoping ('13 commodities/metals/indices CFDs') makes it evident when this tool is relevant, and the paid/auth constraints signal it should be used only with appropriate credentials or payment intent. It doesn't explicitly name alternatives or exclusions, but the context is clear enough.
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.