ensotrade

Fetch Series

fetch_series

Raw time series for one field on one symbol, oldest-first, capped at 1000/1500 points. Use this to explore what a field actually looks like before wiring it into test_formula. Requires an EnsoTrade Pro API key. symbol e.g. 'SOL' or 'SOLUSDT'.

timeframe="scalp" (default): WDE order-flow microstructure, second-scale. field is one of mid, spread_bp, signal, toxicity, cost_bp, ret_1s_bp, ret_5s_bp, ret_30s_bp, ret_60s_bp (top-level), or ofi1, ofi5, dofi, qi, mp, obi, tf1, tf5, tf30, run, inten, vpin, wall, bid_pr, ask_pr, sigma1m (order-flow features). hours max 168. Coverage is uneven and only covers whatever windows the WDE panel was actively open for a symbol — SOL and BNB have the most logged history, other symbols may return no rows for a recent window; widen hours or try a different symbol if so.

timeframe = "15m", "1h", "4h", or "1d" for day/swing work: real OKX perp candles instead of order-flow logs (always available, not dependent on the WDE panel being open). field is one of open, high, low, close, volume, ret_pct (bar-over-bar % return), range_pct (bar high-low as % of close), body_pct (close-open as % of open), rsi14, vol_ratio (volume vs its 20-bar average). hours max ~1500 bars worth (e.g. up to ~62 days for "1h").

Input Schema

TableJSON Schema

Name	Required	Default
`field`	Yes
`hours`	No
`symbol`	Yes
`timeframe`	No	scalp

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A5/5.0

Behavior5/5

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

With no annotations provided, the description carries full behavioral burden and does so thoroughly. It discloses rate limits (capped points), data availability quirks (uneven coverage, dependency on WDE panel), authentication requirements (Pro key), and behavior for different timeframes, giving the agent a realistic model of what to expect.

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

Conciseness5/5

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

Although long, every sentence adds critical detail. The structure is logical: overall purpose, usage context, then per-timeframe parameter specifics. There is no filler or repetition, making the length justified and well-organized.

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

Completeness5/5

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

The tool has an output schema (though not shown), so return format is covered. Given the complexity of multiple timeframes and field lists, the description is impressively complete — it even addresses edge cases like missing data. No agent-relevant detail is omitted.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully explain parameters, and it does. It provides examples for symbol, enumerates valid field values for each timeframe, explains hours max, and clarifies the default and allowed timeframe options. Nothing about the parameters is left ambiguous.

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 it fetches raw time series data for a single field and symbol, with specific details like oldest-first ordering and point caps. It distinguishes itself from sibling tools by specifying its role as a data exploration tool for test_formula, making its purpose unambiguous.

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

Usage Guidelines5/5

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

The description explicitly says to use it to explore a field before wiring it into test_formula, and provides guidance on when to use each timeframe mode (scalp vs. daily/swing). It also explains what to do if data is missing (widen hours or try another symbol), effectively teaching the agent when and how to use this tool.

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

A3.8/5.0

Disambiguation4/5

Most tools target distinct analytics functions (order flow, execution cost, strategy testing, market scans). A few pairs overlap—mass_map/mass_scan, market_rotation/top_movers, and market_snapshot/perp_dashboard—but descriptions clarify differences. No tools are truly indistinguishable.

Naming Consistency3/5

Naming style is mixed: some tools use verb+noun (explain_move, fetch_series, get_funding) while others use descriptive noun phrases (carry_pressure, currency_leg, exit_capacity). The names are readable and meaningful, but the pattern is not uniform. It is not chaotic, but lacks a single consistent convention.

Tool Count4/5

22 tools is on the heavier side but reasonable for a comprehensive crypto analytics suite covering market data, execution analysis, risk, and strategy testing. Each tool serves a distinct purpose and contributes to the overall scope. It is not excessive enough to feel bloated.

Completeness5/5

The tool set covers the full analytics lifecycle: market overview, order-flow explanation, execution routing and cost, position limits, options, and strategy backtesting. There are no obvious dead ends; users can research, test, and validate strategies. The coverage is thorough for the stated trading-analytics domain.

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