Spot BTC & ETH ETF daily net flows (institutional demand, USD millions) — latest + 30-day history ($0.005 USDC)
| Name | Required | Description | Default |
|---|---|---|---|
| payment | No | Optional. Base64 x402 payment payload you signed yourself (ERC-3009 TransferWithAuthorization, USDC on Base). Call without it once to receive the payment requirements, sign those, then call again with this argument. This server forwards the payload and never holds a key. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / paymentAdded value: +{
+ "description": "Optional. Base64 x402 payment payload you signed yourself (ERC-3009 TransferWithAuthorization, USDC on Base). Call without it once to receive the payment requirements, sign those, then call again with this argument. This server forwards the payload and never holds a key.",
+ "type": "string"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must carry behavioral weight. It does disclose the key dimensions 'latest + 30-day history' and a cost of $0.005 USDC. However, it does not state whether a signed x402 payment payload is required, what the output shape looks like, or whether the tool is fully read-only.
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 one compact sentence with the data, units, lookback, and cost presented in a sensible order. Every word contributes to understanding or invoking the tool, 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 single-parameter, paid data endpoint this is nearly complete: metric, units, asset universe, time span, and price are all present. The remaining gap is that no output response shape is described, and the signed-payment precondition is only evident from the schema, not from the tool description itself.
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 single payment parameter is already fully described in the schema, giving a baseline of 3. The description adds extra semantic value beyond the schema by explicit billing the cost: $0.005 USDC, which an agent cannot infer from the payment parameter description alone.
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 names a concrete resource: BTC & ETH ETF daily net flows, measured in USD millions, with a 30-day lookback. It also labels those flows as institutional demand, which distinguishes this from sibling tools like get_crypto_prices, get_funding_rates, or get_sentiment.
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 phrase 'institutional demand' provides clear context for when this tool is relevant: users investigating ETF-driven institutional flows in BTC and ETH. It does not explicitly mention alternatives or exclusions, but the domain is specific enough that an agent can route properly without deeper sibling comparison.
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 clearly distinct metrics, but the paid/free sample pairs (get_crypto_prices/try_crypto_prices, etc.) and overlapping summary tools (get_market_overview, get_market_pulse, get_overnight_risk_brief) create some selection ambiguity. Descriptions list components, so an agent can disambiguate with effort, but the boundaries between bundles and single-purpose tools are not always obvious.
Tool names consistently follow a verb_noun pattern: get_ for data retrieval, try_ for free samples, and watch_condition for persistent monitoring. All names use lowercase snake_case with no mixed conventions, making the naming predictable and easy to navigate.
34 tools is well above the comfortable range for a typical MCP server and feels heavy even for a broad crypto data domain. The count is inflated by paid/free sample duplicates and multiple bundle variants that could have been consolidated.
The surface covers a wide range of crypto data: prices, funding, sentiment, arbitrage, derivatives, on-chain metrics, signal history, and monitoring. Minor gaps exist—such as no obvious generic signal discovery tool or order-book/trade-level data—but the core domain of market data with checkable records is thoroughly served.