Prediction-market superforecaster scan (Polymarket/Manifold/PredictIt) [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/predmarket.]
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It transparently discloses the paid nature, cost per credit, the 402 payment envelope for uncredentialed calls, and the need to set X-API-KEY or pay x402 out-of-band. This gives agents critical behavioral information beyond what the schema provides, though it omits details about rate limits or response behavior.
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 dense sentence that front-loads the purpose and then provides essential payment/auth details. While the URL and cost range add length, they are operationally relevant. No filler words or redundant information; it is efficient though slightly heavy with pricing specifics.
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 description adequately covers purpose, cost, auth, and points to instruments for parameter docs, which is good for a paid scan endpoint. However, with no output schema and no explanation of what the 'superforecaster scan' returns (e.g., opportunities, odds, confidence scores), agents are left guessing about the response shape. This is a meaningful gap.
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 tool has 0 documented parameters, so baseline is 4 per the rubric. The schema description itself points to the free 'instruments' tool for per-endpoint parameter documentation, which compensates for the lack of explicit parameters. The main tool description adds no parameter-level detail, but that is acceptable given there are no params to document.
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 identifies a specific action and resource: 'Prediction-market superforecaster scan' over Polymarket/Manifold/PredictIt. This distinguishes it from sibling scan tools like scan_crypto and scan_forex, and from signal_polymarket. The verb 'scan' plus the named platforms make the scope unambiguous.
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 prediction-market scanning as the use case and gives clear payment prerequisites, but it does not explicitly say when to prefer this tool over alternatives such as signal_polymarket or other scan_* tools. No alternative tools are named and no exclusion criteria are provided.
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.