get_asset_flowsGet daily net ETF flow history for a single crypto asset over the last 30 days. Returns a time series of net USD millions per day (positive = inflow, negative = outflow).
| Name | Required | Description | Default |
|---|---|---|---|
| asset | Yes | Asset to fetch: btc, eth, sol, xrp, hyp, doge, link, avax, hbar, ltc, bnb, dot, sui |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, and the description adds meaningful behavioral context: it returns a daily time series, uses net USD millions, and defines sign semantics (positive = inflow, negative = outflow). No pagination or missing-data behavior is described, but for a simple read-only tool this is sufficient.
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 no wasted words. It front-loads the core action and resource, then immediately clarifies the return format and sign convention. Every sentence contributes useful 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?
With one well-specified parameter and a read-only annotation, the description is complete for selecting and invoking the tool correctly. It explains the data range, unit of measurement, and interpretation of positive/negative values, which is especially important because there is no output schema.
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 description coverage is 100%, so the single 'asset' parameter is fully documented by the schema's enum and description. The tool description only reinforces that it targets one crypto asset, adding little beyond the schema; the baseline of 3 is appropriate.
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 uses a specific verb ('Get'), names a concrete resource ('daily net ETF flow history for a single crypto asset'), and adds temporal scope ('over the last 30 days'). This clearly distinguishes it from sibling tools like get_prices or get_flows_summary.
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 makes the use case clear: fetch daily ETF flow history for one asset over a 30-day window. It does not explicitly name alternatives or exclusion conditions, but the single-asset scoping and ETF-specific language provide strong contextual guidance against using it for multi-asset summaries or price data.
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.
Each tool has a clear primary focus: single-asset historical flows, composite sentiment index, latest cross-sectional flows, prices, and weekly aggregation. The only minor overlap is between get_flows_summary and get_weekly_analytics, but their descriptions make the distinction obvious.
All tool names follow a consistent get_<noun> pattern using snake_case, with clear descriptive suffixes like asset_flows, flows_summary, and weekly_analytics. There is no mixing of conventions or vague verbs.
Five tools is well-scoped for a read-only crypto ETF analytics server. Each tool covers a distinct data need without redundancy, and the count is appropriate for the narrow domain.
The set covers the core query surface for ETF flow data: recent daily snapshot, single-asset history, weekly aggregates, sentiment index, and prices. Minor gaps exist, such as no multi-asset historical comparison or explicit asset list endpoint, but these are unlikely to cause major agent failures.