24h volatility for BTC/ETH/SOL ($0.002 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 carries the behavioral burden. It does transparently signal that the call has a USD-denominated cost ('$0.002 USDC'), which is useful. However, it does not explicitly state that data is returned on first call versus a payment-requirements response, although the input schema does describe the two-step x402 payment flow.
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 single, front-loaded phrase that states the metric, the asset scope, and the price with no filler. Every clause earns its place, and the missing details live in the parameter schema.
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?
The description and schema together cover the metric, the assets, the payment flow, and the price, which is most of what an agent needs for this simple tool. However, there is no output schema and no statement about the return value, so an agent must guess whether the result is a percentage, a list of values, or a time series.
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 input schema has 100% parameter-documentation coverage: it fully explains the optional x402 payment payload, the signing requirement, and the server behavior. The description adds the exact price but not much about how to construct the payment parameter, so the schema rightfully does the heavy lifting.
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 specific metric ('24h volatility') and the exact asset universe ('BTC/ETH/SOL'), so an agent can tell this from the many price, sentiment, and technical-indicator siblings. Although it lacks a verb, the tool name 'get_volatility' supplies the action and the phrase names a concrete, measurable resource.
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 gives no explicit guidance about when to call this tool versus alternatives like get_technical_indicators, get_crypto_prices, or get_historical. It implies the context through the metric name, but it does not state exclusions, preferred use cases, or how to choose between volatility norms and related tools.
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.