aipricepatterns

pattern_search

Search for similar historical price patterns in the RLX database.

Input Schema

TableJSON Schema

Name	Required	Description
`f`	No	Forecast horizon in bars
`q`	No	Query length in bars
`sort`	No	Sort mode for results
`limit`	No	Maximum number of matches to return
`symbol`	No	Ticker symbol (e.g., BTCUSDT)
`compact`	No	Default true: strips per-match value arrays and full forecast paths, returning match metadata, horizon-end price targets, and the calibrated outcome distribution (~4x fewer tokens). Set false for full arrays.
`anchorTs`	No	Target timestamp to search around
`interval`	No	Timeframe (e.g., 1h, 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.
`embeddingMode`	No	Pattern embedding mode: priceShape uses normalized closes, featureV1 uses OHLCV/context, neuralV1 uses the experimental ONNX encoder

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedJul 6, 2026

TDQS

C2.8/5.0

Behavior2/5

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It only states the action without revealing output format, pagination, rate limits, authentication needs, or whether the operation is read-only. It implies a read/search operation but does not explicitly confirm safety or side effects.

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

Conciseness3/5

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

The description is a single concise sentence with no wasted words. However, it is under-specified for a tool with 10 parameters and no other context, making it too terse to be considered well-structured.

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

Completeness2/5

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

With no output schema and no annotations, the description must provide context about what the tool returns and how it behaves. It does neither. A complex tool like this needs an overview of use cases, result format, and any special considerations, all of which are missing.

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

Parameters3/5

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

All 10 parameters have detailed descriptions in the input schema (100% coverage), so the description need not add parameter details. The description itself adds no additional semantic meaning beyond the schema, so the baseline of 3 applies.

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

Purpose4/5

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

The description uses a specific verb ('Search') and resource ('similar historical price patterns in the RLX database'), making the core purpose clear. However, it does not differentiate from the sibling tool 'find_market_analogs', which appears to serve a similar function.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool over alternatives such as 'find_market_analogs' or 'search_by_sketch'. There are no use cases, prerequisites, or exclusions mentioned.

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.

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