FREE — the full guru roster board: every desk's machine-graded record (roi_units = total R), daily/weekly/monthly buckets, retired desks frozen. No auth, no payment.
| 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 and delivers: it discloses access constraints ('No auth, no payment'), data semantics ('roi_units = total R'), and behavioral details (machine-graded, daily/weekly/monthly buckets, retired desks frozen). This is substantive beyond a simple 'get leaderboard' statement.
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 two sentences with zero waste. It front-loads 'FREE' and 'full guru roster board', then delivers key details (buckets, frozen desks, free access) without redundancy or filler.
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?
For a 0-parameter, read-only tool with no output schema, this description is fully complete. It explains what data is returned (all desks, machine-graded, total R, buckets), the access status (free/public), and special cases (retired desks frozen). No significant gaps remain.
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 zero parameters, so the schema provides no parameter information. Per baseline for 0-param tools, a score of 4 is appropriate. The description does not need to explain parameters since none exist.
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 provides 'the full guru roster board' with 'every desk's machine-graded record', including daily/weekly/monthly buckets and frozen retired desks. This is specific about the resource and its content, distinguishing it from siblings like scan_* or signal_* that serve different analytical purposes.
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 by emphasizing 'FREE' and 'No auth, no payment', suggesting it is a public, zero-cost lookup. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or competing tools like track_record.
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.