Perp-vs-spot basis for any USDT pair: premium/discount %, contango/backwardation read, funding context. Costs $0.01 USDC per call (x402, Solana mainnet).
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | USDT perp symbol e.g. SOLUSDT |
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 present, the description carries the full burden of disclosure. It goes beyond the name by revealing the paid nature of the call, the payment transport (x402, Solana mainnet), and the returned metric family. It does not mention auth or rate limits, but the cost and network context are meaningful behavioral disclosures.
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 filler. The core purpose is front-loaded, the output metrics are compactly listed, and the cost/payment detail is included without bloating the description. 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 single-parameter tool with no output schema and no annotations, the description covers the essential context: what is measured, what is returned, and what it costs. It could name a concrete example response shape, but the listed metrics (premium/discount %, contango/backwardation, funding context) give enough for an agent to invoke and interpret the result.
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?
Schema coverage is 100%, so the symbol parameter is already documented. The description adds useful context by clarifying that the symbol is a USDT perp pair and that the tool computes perp-vs-spot basis, which helps disambiguate the parameter. This is marginal value above the schema, so a 3 is appropriate.
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 uses a specific verb and resource: 'Perp-vs-spot basis for any USDT pair.' It lists concrete outputs (premium/discount %, contango/backwardation read, funding context), which clearly distinguishes it from sibling tools like get_funding_rate or get_spread_arb. The tool's function is immediately identifiable without needing to inspect the schema.
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 'for any USDT pair' gives clear scope for when to call it, and the cost note ('Costs $0.01 USDC per call') is an important practical constraint. It does not explicitly mention alternatives or exclusions, but the purpose is specific enough that an agent can infer when basis data is needed.
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.