Peg deviation broken out by trading session (open, premarket, afterhours, overnight, weekend): mean, p95, max bps and median liquidity per session, with the worst off-hours window flagged. Market-open acts as the control. Costs $0.03 USDC per call (x402, Solana mainnet).
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | lookback 1-30, default 7 | |
| symbol | Yes | Tokenized equity symbol e.g. CRCLx, MSTRx, COINx |
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 behavioral disclosure burden. It adds meaningful operational context: computed metrics per session, the control baseline, the worst-window flag, and the $0.03 USDC cost via x402 on Solana mainnet. It does not cover response format or error behavior, but the core behavioral traits are disclosed.
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 two sentences with no filler: the first packs analytical scope and output metrics, the second adds the control baseline and cost. It is front-loaded and every clause 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?
Given no output schema and no annotations, the description adequately explains what the tool returns: per-session mean, p95, max bps, median liquidity, and a flagged worst window. A response format example would make it fully complete, but the essentials for selecting and invoking the tool are present.
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 already documents both parameters with 100% coverage, so the baseline applies. The description adds no parameter-specific meaning beyond what the schema provides for symbol and days.
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 states the tool's exact function: peg deviation broken out by trading session, with specific metrics and a flagged worst off-hours window. This distinguishes it from sibling peg-related tools through the explicit session dimension and statistical detail.
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 implies the tool should be used when session-level peg deviation analysis is needed, especially with market-open as a control. However, it never explicitly states when to prefer this over alternatives like get_peg_deviation, nor does it say when not to use it.
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.