FREE SAMPLE, no payment: real-time prices for BTC, ETH, SOL, BNB, XRP. Rate-limited to 30 requests/min. The paid get_crypto_prices ($0.002 USDC) is the same data without the limit.
| Name | Required | Description | Default |
|---|---|---|---|
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it discloses the key behavioral traits: no payment required, a 30 req/min rate limit, and identical data to the paid sibling. It does not describe the exact output shape, but the return content (prices for five assets) is implied clearly enough.
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 compact and front-loaded with the most important fact ('FREE SAMPLE, no payment'). Every sentence earns its place: what data, the rate limit, and the paid alternative.
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 zero-parameter, no-output-schema tool, the description is complete: it names the covered assets, states real-time pricing, discloses the rate limit, and routes to the paid sibling when needed. An agent has everything required to decide whether and when to call it.
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 tool has zero parameters, so the baseline is 4. The description adds no parameter-level detail because there is nothing to document; the schema already fully covers this by declaring an empty properties object.
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 states a specific verb ('prices'), a precise resource set (BTC, ETH, SOL, BNB, XRP), and a clear delivery mode ('real-time'). It also distinguishes the tool from the paid get_crypto_prices sibling, so an agent can tell them apart without opening schemas.
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 explicitly marks this as a free, rate-limited sample and names the paid alternative get_crypto_prices as the version without the limit. This gives the agent clear selection criteria: use this for free/sample needs and the paid tool when the rate limit is unacceptable.
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.
