Live on-chain events (fresh launches, graduations, whale big-buys). Last 30min free; historical is paid. K3 event stream.
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | ||
| types | No | comma list: launch,graduation,whale |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It does reveal the free vs. paid time window and the live-event nature, but it does not mention authentication, rate limits, pagination, output format, or what 'K3 event stream' means. This is useful but incomplete.
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 compact two-part statement that front-loads the core value proposition and then adds the key constraint. Every phrase contributes meaning, with no filler or repetition.
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 simple tool with two optional parameters and no output schema, the description covers the main usage and cost boundary but leaves gaps: 'since' is not clearly explained, the output format is unspecified, and the 'K3 event stream' reference is unexplained. It is adequate for basic selection but not fully self-sufficient.
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 documents 'types' as a comma list of launch, graduation, and whale, and the description reinforces those categories. However, the integer 'since' parameter has no schema description and the description only implies time-sensitivity via 'Last 30min free; historical is paid', which is not explicit enough to define its format or meaning.
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 the resource as live on-chain events and lists the event categories (launches, graduations, whale big-buys), so an agent can understand what the tool returns. It lacks an explicit verb like 'retrieve' or 'list', and it does not explicitly contrast itself with sibling tools such as copy_signals, so it falls short of a 5.
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 'Last 30min free; historical is paid' sentence gives clear context about when the tool is usable and its cost boundary. It does not explicitly name alternative tools for historical data, but the context is sufficient for an agent to infer this is the live-event endpoint.
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.
Most tools target distinct actions (build a swap, check a mint, verify a claim, fetch pricing), and the descriptions make boundaries clear. The main risk is mild confusion between check_mint/score_mint and copy_signals/recent_events, as both pairs sit in adjacent signal/safety territory, but they are not interchangeable.
Most names follow a verb_noun pattern (build_swap, check_mint, score_mint, verify_claim, track_record). guard_pricing and recent_events break that pattern slightly, but the naming remains predictable and readable overall.
Nine tools is well within the ideal range and each tool earns a place in the safety/signal/trading workflow. It is neither bloated with near-duplicates nor too thin to define a useful server.
The server covers a coherent lifecycle: discovery (recent_events/copy_signals), safety and validation (check_mint/score_mint/verify_claim), execution (build_swap), and outcome auditing (track_record). Minor gaps exist, such as an EVM swap builder to complete the Robinhood check path, but the core workflow is usable.