get_smart_money_inflowTokens ranked by NET smart-money inflow in US dollars over the last 24 hours, from wallets Binance tracks as consistently profitable. Reports the number of distinct wallets behind each figure — a large inflow from one wallet is a position, not a consensus. BSC. Use when asked what smart money is buying or where informed flow is going.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Rows to return (1-15). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations: it notes that the data comes from 'wallets Binance tracks as consistently profitable', explains the wallet-count metric ('a large inflow from one wallet is a position, not a consensus'), and specifies the chain is BSC. Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds no contradiction and provides key interpretive guidance for the returned data.
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 compact and well-structured: 3 sentences. The first sentence states the core function, the second adds critical interpretive detail, and the third gives usage guidance. No filler or redundancy. It is front-loaded with the most important information.
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 covers the essential aspects: what the tool does, the data source, the key metric's interpretation, the blockchain (BSC), and usage context. Given that the tool has only one parameter, full parameter coverage, and no output schema, the description is quite complete. It does not describe output format or pagination, but for a simple list tool with good annotation coverage, this is not a significant gap. A small deduction for not stating the time range explicitly in the first sentence but it is implicit in 'last 24 hours'.
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 only one parameter (limit) with 100% coverage, meaning the schema already documents it fully (rows to return, default 8, range 1-15). The description does not need to repeat this. However, the description adds no additional meaning about the parameter, but the baseline for 100% coverage is 3, and the description's mention of the 'number of distinct wallets' is actually about the output not the parameter. The score is raised to 4 because the parameter is trivial and the description adds context about what the returned rows mean (net inflows and wallet counts), which indirectly helps the agent understand how limit interacts with the results.
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's purpose: 'Tokens ranked by NET smart-money inflow in US dollars over the last 24 hours'. It identifies the specific resource (tokens ranked by smart-money inflow) and the metric (net inflow in USD), and provides a distinct data source (wallets Binance tracks as consistently profitable). It distinguishes from the sibling tool get_smart_money_flow by being more focused on net inflow over 24 hours and by reporting wallet counts.
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 explicitly states when to use the tool: 'Use when asked what smart money is buying or where informed flow is going.' This advises the agent when the tool is appropriate, and implicitly distinguishes it from alternatives like get_smart_money_flow or get_trade_signals. While it doesn't say 'when not to use', the explicit usage context is strong guidance.
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.
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.