Full market state in one call: SOL+BTC prices, funding, Fear & Greed, long/short positioning, open interest, and liquidation stats. The complete pre-trade picture. Costs $0.01 USDC per call (x402, Solana mainnet).
| 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?
No annotations are provided, so the description carries the full burden. It discloses a concrete behavioral trait—cost of $0.01 USDC per call via x402 on Solana mainnet—and enumerates the metrics returned. It does not specify exact response structure or units, but for a parameterless read-style tool, the covered behaviors are meaningful and clear.
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?
Two sentences with no wasted words. The aggregate purpose and metric list are front-loaded, and the cost/network note is appended efficiently. Every sentence earns its place.
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 parameterless aggregate tool with no output schema, the description covers what the agent gets, what it costs, and where it runs. It lacks exact field names and units, but the listed metric categories provide enough context to justify invocation and anticipate the response shape.
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 is empty with zero parameters, so there is no parameter meaning for the description to add. The baseline for a zero-parameter tool is 4, and the description appropriately focuses on the return contents instead of arguments.
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 clearly identifies a composite resource: a single-call snapshot of SOL+BTC prices, funding, Fear & Greed, long/short positioning, open interest, and liquidation stats. This distinguishes it from the many single-metric sibling tools like get_sol_price, get_funding_rate, and get_liquidation_stats.
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 frames this as the 'complete pre-trade picture' and 'full market state in one call,' which clearly signals when an agent should use it: when it needs a broad multi-metric view before trading. It does not explicitly name alternatives like get_btc_price or get_open_interest for single-metric needs, but the aggregate framing strongly implies that distinction.
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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Many tools are tightly scoped and cross-referenced, but the set contains overlapping families: liquidation tools (alert/scan/history/stats/leaders/recent/heatmap) and redundant snapshots like get_market_snapshot vs get_trade_context, get_last_liquidation vs get_recent_liquidations, and get_cascade_forecast_free vs get_cascade_forecast. Agents will need to read descriptions carefully to avoid misselection.
All tool names follow a consistent get_<domain>_<detail> snake_case pattern, which makes the API predictable. The only real deviations are the bare 'pricing' tool and the 'free' suffix on taster variants.
At 52 tools, this far exceeds the 3-15 well-scoped range and crosses the 50-tool extreme threshold. The count is inflated by numerous paid/free taster pairs and many overlapping liquidation variants.
The surface covers prices, funding, open interest, orderbooks, liquidations, wallet/token data, Solana network health, DeFi TVL, and stablecoin flows—broad coverage for a crypto data feed. Gaps like historical OHLC/price candles, a machine-readable symbol list, and pagination endpoints are workable around but would round it out.