aipricepatterns

find_market_analogs

Find historical price patterns similar to the current (or specified) market state. Returns a list of past dates when the same pattern occurred, the price outcome after each analog, and aggregate statistics (win rate, median return, percentile range). Use cases: (1) pre-news analysis — filter by timeOfDayUTC to find analogs that happened near a specific event time (e.g., FOMC at 14:00 UTC); (2) regime research — understand historically what happens after this pattern; (3) Polymarket context — combine with get_polymarket_probabilities to validate signal with historical evidence. Returns a plain-English summary suitable for agent reasoning.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`f`	No
`q`	No
`limit`	No
`symbol`	Yes	Ticker symbol, e.g. BTCUSDT
`context`	No
`session`	No
`interval`	No	Candle interval	15m
`token_id`	No	Optional Manus access token. Paid tools use tokenized service access, not a monthly subscription: when token_id is omitted the server returns payment_required with a Solana Pay invoice, and after payment you retry with the same token while the server uses Manus token/resolve to recover pending access.
`weekdays`	No
`timeOfDayUTC`	No
`timeRangeUTC`	No
`minSimilarity`	No
`anchorTimestamp`	No
`timeWindowMinutes`	No

TDQS

A4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses return structure (list of dates, outcomes, aggregate stats, plain-English summary) and mentions a specific filtering behavior (timeOfDayUTC). It does not discuss error/payment behavior or edge cases, but adds meaningful behavioral context.

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?

The description is well-structured and front-loaded: purpose in the first sentence, return details next, then enumerated use cases. It is neither terse nor wordy, and every sentence adds value.

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?

Given the high complexity (14 params, nested objects, no output schema), the description provides useful high-level context and use cases but does not sufficiently explain the parameter space or how to use more advanced filters. It is adequate for basic use but incomplete for full utilization.

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

Parameters2/5

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

Schema description coverage is only 21%, and the description only meaningfully explains timeOfDayUTC among the many parameters. Parameters like f, q, minSimilarity, timeRangeUTC, weekdays, and anchorTimestamp are not explained in the description, leaving significant gaps in understanding how to configure a good query.

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 tool finds historical price patterns similar to the current or specified market state and lists the concrete outputs (past dates, outcomes, aggregate statistics). This specific verb+resource definition distinguishes it from sibling tools like backtest_strategy or detect_market_regime.

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 gives three explicit use cases: pre-news analysis (with timeOfDayUTC filtering), regime research, and combining with get_polymarket_probabilities. It lacks explicit 'do not use when...' exclusions or direct comparison with alternative tools, but the context is clear.

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

B3.2/5.0

Disambiguation2/5

Several tools inhabit overlapping territory: find_market_analogs, pattern_search, search_by_sketch, and get_candle_market_snapshot all relate to historical pattern matching, while get_trading_decision, get_trader_decision_v2, and get_live_polymarket_trade_decision all produce trade-oriented decisions. The descriptions add context, but an agent could still easily pick the wrong tool for a given request.

Naming Consistency3/5

The tools are consistently snake_case and mostly readable, but the naming conventions are mixed: many tools use get_<noun>, while others start with verbs like backtest, detect, find, forecast. Minor irregularities such as pattern_search and the v2 suffix in get_trader_decision_v2 also reduce predictability.

Tool Count4/5

Fifteen tools is within a reasonable size, and the server covers a broad domain: pattern search, regime detection, backtesting, track records, private datasets, live Polymarket decisions, and documentation. The count is not excessive, but some tools are functionally redundant enough that the set could be tightened.

Completeness4/5

The tool surface covers the main evidence workflow well: discovering patterns, analyzing analogs, backtesting strategies, checking track records, and producing trading decisions. Minor gaps remain around private dataset management and there is no separate low-level raw candle query tool, but most core user journeys are supported.

Resources

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