binance_get_adl_riskRating risiko Auto-Deleveraging (ADL) LOW/MEDIUM/HIGH per pair (agregat level symbol, bukan posisi kamu), LANGSUNG dari Binance native. PENTING: di-update tiap 30 MENIT, bukan real-time -- konteks risiko umum, bukan keputusan eksekusi presisi detik.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Simbol pair Binance Futures, contoh: BTCUSDT, ETHUSDT. Harus pair perpetual yang terdaftar di Binance USDS-M Futures. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given annotations already declare readOnlyHint and openWorldHint, the description adds valuable behavioral context: the 30-minute update cadence, the aggregate symbol-level scope, and the non-real-time nature. This goes beyond the annotations and clarifies important limitations.
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, front-loaded with the result values and core scope, then a critical caveat. Every sentence earns its place with no redundancy.
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 simple one-parameter read-only tool with no output schema, the description is complete: it states the return categories (LOW/MEDIUM/HIGH), scope, source, and update limitation. No essential behavioral or usage context is missing.
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 schema fully describes the single 'symbol' parameter with pattern, length limits, and examples, so the description does not need to add much. The description's 'per pair' phrasing aligns with the schema but adds no new semantic detail.
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: 'Rating risiko Auto-Deleveraging (ADL) LOW/MEDIUM/HIGH per pair' directly from Binance. It also distinguishes the tool by clarifying it is symbol-level aggregate risk, not position-level, separating it from other analysis tools.
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 provides clear usage context by stating the data updates every 30 minutes and is for general risk context, not precise execution decisions. It effectively communicates when not to rely on it, though it does not explicitly name alternative tools.
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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Most tools map to a distinct Binance metric or analytic concept, and descriptions explicitly contrast near-neighbors (spot vs futures, snapshot vs delta, 'BEDA dari...' notes). A few pairs could still be confused—`binance_get_basis` vs `binance_get_basis_history` and `binance_get_agg_trades` vs `binance_get_recent_trades`—but their purpose differences are explained well enough for careful agents.
The dominant pattern is `binance_<verb>_<object>` in snake_case, with consistent complementary pairs like `get_*` and `get_*_history`. There are minor style breaks: `orderbook` vs `order_book`, the `whalescope_*` prefix, and `whalescope_full_pipeline` which lacks a verb, but the overall structure is readable and predictable.
56 tools cross the explicit '50+ tools' extreme threshold. Although the Binance Futures domain is broad, many tools are single-endpoint or single-metric wrappers—multiple klines variants, order book variants, and ticker variants—that could be consolidated into parameterized composite tools. The surface is far too large for most agents to navigate efficiently.
The public market-data and analytics surface is remarkably complete: klines, funding, open interest, long/short ratios, top-trader data, liquidations, basis, order book behavior, regime detection, and full pipeline scoring are all covered. The main gaps are documented limitations such as unavailable liquidation-by-price data and non-public account/execution tooling, but agents can work around them without dead ends.