FREE SAMPLE, no payment: perpetual funding rates. Rate-limited to 30 requests/min. The paid get_funding_rates ($0.002 USDC) is the same data without the limit.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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, the description carries the full behavioral disclosure burden. It reveals two key operational traits: the tool requires no payment and is rate-limited to 30 requests/min. Not specifying response format is acceptable for a zero-parameter sample endpoint.
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?
Three compact segments deliver distinct value: what the tool is, the rate limit, and the paid alternative. Every sentence earns its place and the most differentiating information is front-loaded.
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 no-parameter data-sample tool, the description is complete: the agent knows what data to expect, the limiting constraint, and when to switch to the paid sibling. No additional detail is needed to invoke it correctly.
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 for this dimension is 4. The description does not need to explain parameter meaning because there is nothing to configure.
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?
Clearly identifies the resource as perpetual funding rates and frames this tool as a free sample. It explicitly contrasts with the paid get_funding_rates sibling, so an agent can tell them apart immediately.
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?
Names the alternative tool, get_funding_rates, and states the trade-off: same data but the paid version removes the rate limit. This gives the agent an explicit selection criterion between the free and paid variants.
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.
