Every tokenized US equity we track, ranked by off-hours peg risk: p95 and max deviation bps, market-open deviation as control, median liquidity. Dead pools are excluded rather than reported as perfect pegs. Costs $0.05 USDC per call (x402, Solana mainnet).
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | lookback 1-30, default 7 | |
| min_liquidity_usd | No | filter out thinner pools |
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 transparency burden. It clearly discloses that dead pools are excluded to avoid misleading perfect-peg readings, that the call costs $0.05 USDC on x402/Solana, and that results are ranked with specific deviation and liquidity fields. This is unusually candid about data handling and cost.
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 sentences deliver the core behavior, ranking criteria, exclusions, and pricing without fluff or restating the tool name. The most important ranking information is front-loaded, and each sentence adds distinct information.
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 covers the returned ranking fields, ordering, exclusion behavior, cost, and network, which is substantial for a tool with zero required parameters and no output schema. It does not specify pagination or exact response envelope, but the essential calling context is complete enough for an agent to invoke and interpret 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?
Both parameters are already fully described in the schema (lookback range/default and liquidity filter), so the description adds little parameter-level guidance. The schema coverage is 100%, so the baseline of 3 applies; the description doesn't need to compensate.
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 specifies a concrete resource: every tokenized US equity tracked, with a clear ranking criterion (off-hours peg risk) and key output fields. This distinguishes it from single-asset or session-level peg tools in the sibling list, even though no sibling is named.
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 phrase 'Every tokenized US equity we track' makes the broad survey use case clear: call this to get the full ranked universe rather than one asset's peg behavior. It does not explicitly state when not to use it or name alternatives, so the guidance is contextual rather than exclusionary.
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.