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Latest Crypto ETF Flows

get_flows_summary
Read-only

Get the latest-day net ETF inflow/outflow (USD millions) for every tracked crypto asset: BTC (Bitcoin), ETH (Ethereum), SOL (Solana), XRP, HYP (Hyperliquid), DOGE (Dogecoin), LINK (Chainlink), AVAX (Avalanche), HBAR (Hedera), LTC (Litecoin), BNB, DOT (Polkadot), SUI, NEAR, TRX (TRON) and ZEC (Zcash). Positive = inflow, negative = outflow. Flows are published one business day in arrears, so referenceIsToday is normally false: the figures belong to referenceDate, the last trading day published, not to the current date. On Mondays the freshest data is Friday's. totalUsdM is the sum across all assets. Figures are asset-level totals with no per-fund breakdown; per-fund tables (IBIT, FBTC, ETHA and others) are published at https://cryptoetf.today/en/-etf-flows, slug = bitcoin, ethereum, solana, xrp, hype, dogecoin, chainlink, avalanche, hedera, litecoin, bnb, polkadot, sui, near, tron or zcash.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the description needn't restate safety. It adds useful behavioral context about publication lag and lack of per-fund breakdown, but does not describe return format, pagination, or error behavior — acceptable given the explicit annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It front-loads the core purpose, then explains the publication lag and alternative source in a logical flow. The long asset enumeration is necessary for clarity but makes it slightly verbose; otherwise every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only summary tool with no output schema, the description serves as a compact data dictionary: it defines the sign convention, units, lag, and coverage. It could explicitly state the response shape (e.g., a map keyed by asset), but it is otherwise complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters, the baseline is 4. The description does not need to explain any input parameters, and it correctly focuses on output semantics (inflow/outflow sign, units, totalUsdM).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the exact verb and resource: 'Get the latest-day net ETF inflow/outflow (USD millions) for every tracked crypto asset,' then enumerates the assets. This unmistakably distinguishes it from siblings like get_asset_flows or get_prices.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly explains when the data applies (published one business day in arrears, referenceIsToday normally false, Monday yields Friday data) and gives the alternative URL for per-fund breakdowns. It doesn’t explicitly name when to use get_asset_flows instead, but the scope is clear enough that an agent can infer.

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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