scan_launchpadsNew and trending token launches: Pump.fun (Solana), Zora (Base), or the Robinhood Chain market catalog. [Paid: $0.25–$0.40 per call from your Einstein credit balance. Free alternatives exist for several of these — see list_einstein_capabilities.]
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum results (1-100). | |
| venue | No | Which launch venue to scan. | pumpfun |
| timeframe | No | Lookback window. | 24h |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive traits. The description adds valuable cost context ($0.25–$0.40 per call) and notes free alternatives, but doesn't disclose return format or pagination behavior. This adds some context beyond annotations but not rich detail.
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 concise sentences. The first front-loads the purpose, and the second adds a critical cost caveat. No wasted words.
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?
Complexity is low with 3 optional params and no output schema. The description covers the core function and cost, and annotations cover safety. It could mention what a result contains but is complete enough for a simple scanner 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?
Schema covers 100% of parameters, providing a baseline of 3. The description enriches the venue parameter by mapping Pump.fun to Solana, Zora to Base, and mentioning the Robinhood Chain catalog, adding meaning not present in the 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 clearly states the tool scans for new and trending token launches on specific venues (Pump.fun, Zora, Robinhood Chain). It specifies the resource and action but doesn't explicitly differentiate from siblings like scan_market, though the venue list provides implicit distinction.
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 provides no explicit when-to-use guidance or exclusions. It mentions free alternatives for some venues but doesn't clarify when to prefer this tool over siblings. Usage is only implied by the tool name and venue list.
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.