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Recent Trades (Individual)

binance_get_recent_trades
Read-only

Trade individual terbaru (GET /fapi/v1/trades) — BEDA dari binance_get_agg_trades yang sudah di-aggregate. Lebih granular untuk analisis micro-structure. Default summary (CVD + 15 trade terakhir).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoJumlah trade terakhir (max 1000)
detailNo'summary' (default): metrik turunan + <=10 poin terbaru saja, HEMAT TOKEN. 'full': array/level mentah lengkap seperti sebelumnya. Lihat docs/tool_response_reference.md.summary
symbolYesSimbol pair Binance Futures, contoh: BTCUSDT, ETHUSDT. Harus pair perpetual yang terdaftar di Binance USDS-M Futures.

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already declare readOnlyHint=true and openWorldHint=true, so the description doesn't need to restate safety. It adds valuable behavioral context beyond annotations: the default summary mode returns 'CVD + 15 trade terakhir' (Cumulative Volume Delta and 15 recent trades), and it highlights token efficiency. This supplements the schema and annotations meaningfully, though it could further clarify the exact structure of the summary vs full response.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only three sentences with the key distinction (vs aggregated) front-loaded. It is efficient and avoids fluff. However, the structure could be improved by separating the default behavior into a clearer sentence or bullet, and the mixed-language phrasing ('Terbaru' repeated) slightly reduces clarity. Still, it earns a 4 for being succinct.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must explain the return format. It mentions the default summary returns 'CVD + 15 trade terakhir' and points to docs/tool_response_reference.md, but it does not specify the full structure of either 'summary' or 'full' modes, nor how the 'limit' parameter interacts with summary mode (does it cap at 15 regardless of limit?). An agent may struggle to parse the response without consulting the docs. This is a moderate gap, so a 3 is appropriate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the baseline is 3. The description adds extra meaning: it explains the trade-off between 'summary' and 'full' modes (token savings vs raw data) and mentions that summary includes CVD, which is not in the schema. It also discloses the default summary behavior, giving an agent context beyond the parameter enums. This added insight justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Trade individual terbaru'), the resource (individual trades from GET /fapi/v1/trades), and explicitly differentiates from the sibling tool binance_get_agg_trades which returns aggregated data. The phrase 'Lebih granular untuk analisis micro-structure' further clarifies its distinct purpose, leaving no ambiguity about what this tool does or how it differs from similar tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly contrasts this tool with binance_get_agg_trades, implying it should be used when granular, non-aggregated trade data is needed for micro-structure analysis. It also indicates a default to summary mode to save tokens, suggesting when to stick with the default. It does not explicitly state 'do not use when X' but the comparison provides clear selection guidance. A more explicit 'use this instead of aggregated when you need individual trades' would push this to 5.

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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TDQS

A3.7/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources