aipricepatterns

get_pattern_metrics

Retrieve advanced statistical metrics and forecast distributions for a pattern search result.

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

A3.9/5.0

Behavior3/5

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

The description states 'Retrieve' which correctly indicates a read-only operation. With no annotations provided, the description carries the full burden, but it does not disclose the token_id payment behavior or other side effects (though these are in the schema). This is adequate but not rich for a tool with a significant payment-related parameter.

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 a single, front-loaded sentence with no unnecessary words. It efficiently conveys the core purpose without rambling.

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?

Despite the rich schema, the description is short for a tool with 10 parameters and no output schema. It doesn't describe the return format or the relationship to pattern_search output, but the schema and sibling names provide substantial context, making the description minimally viable.

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?

The schema covers all 10 parameters with descriptions (100% coverage), so the baseline score is 3. The description adds no parameter-level meaning beyond the schema, so it neither improves nor degrades the score.

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 uses the specific verb 'Retrieve' and identifies the resource as 'advanced statistical metrics and forecast distributions' for a pattern search result. This clearly distinguishes it from sibling tools like pattern_search, which likely performs the actual search.

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 phrase 'for a pattern search result' implies the tool should be used after a search, providing clear context for when to use it. However, it does not explicitly name alternative tools or state when not to use it, missing the 'when/not/alternatives' bar for a 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

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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