get_polymarket_whalesLarge and unusually-early positions taken on Polymarket prediction markets, surfaced from the insider-detection scan.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum rows to return (1-25). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which cover the core behavioral safety profile. The description adds useful context that the data comes from an insider-detection scan, but does not disclose other behavioral aspects like response format or pagination, which would be helpful since there is no output schema.
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 sentence that conveys the core purpose and source without redundancy or filler. It is appropriately succinct and front-loaded, making it easy for an agent to parse quickly.
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 simple tool design (one optional parameter, no output schema, clear annotations), the description provides sufficient context about what the tool returns and its origin. It would benefit from explicitly stating the output is a list and any default behavior, but the current level of detail is adequate for basic invocation.
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 only parameter, limit, is fully described in the schema ('Maximum rows to return (1-25).'), achieving 100% schema coverage. The tool description adds no additional parameter semantics, so the baseline score of 3 applies because the schema carries the parameter clarity burden.
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 conveys that the tool returns large, unusually early positions on Polymarket prediction markets, sourced from an insider-detection scan. This distinguishes it from sibling tools like get_hyperliquid_whales by specifying the market and scan context, though it lacks an explicit verb like 'get' or 'list'.
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 mentions the insider-detection scan as the data source, implying a specific use case, but it does not explicitly name alternative tools or provide when/when-not guidance. No exclusions or comparisons to siblings such as track_whales or get_hyperliquid_whales are given, so usage guidance remains implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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With 40 tools, many share overlapping domains: get_smart_money_flow vs get_smart_money_inflow, scan_launchpads vs get_launchpad_radar, track_whales vs get_hyperliquid_whales, and check_token_safety vs analyze_token_security. The detailed descriptions help, but the boundaries are not always clear, making misselection likely.
The tool names employ a wide variety of verbs (get_, analyze_, scan_, track_, find_, generate_, recommend_, run_, list_, ask_, assess_, detect_) with no consistent pattern. While all use snake_case, the inconsistent verb choices and occasional deviations like forecast_chart prevent predictability.
40 tools is well above the typical 3-15 well-scoped range and exceeds the 25+ threshold for 'too many'. While the broad 'crypto intelligence' purpose justifies some breadth, the sheer number makes the surface unwieldy and suggests a lack of focused scoping.
The tool set covers a wide range of crypto intelligence domains: market analysis, forecasting, whale tracking, yield/arbitrage, security checks, prediction markets, backtesting, and even content generation. Missing operations are minor (e.g., no direct portfolio management), but core analysis and data retrieval workflows are well represented.