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Glama

get_candle_track_record

Walk forward the exact chart candle configuration (symbol, interval, q, f) over independent resolved anchors. Compares analogue and volatility-scaled range bands against the unconditional baseline with coverage, confidence intervals, Winkler scores, regime diagnostics, and explicit non-directional signal integrity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fNoExact forward horizon to evaluate: 5, 10, 30, or 50 bars
qNoExact chart pattern length to evaluate: 3, 5, 8, or 12 candles
endTsNoOptional Unix-millisecond cutoff that pins the evaluation dataset for reproducibility
symbolNoSupported chart symbol: BTCUSDT, ETHUSDT, or SOLUSDTBTCUSDT
anchorsNoIndependent resolved anchors to evaluate. Anchors are spaced f bars apart (10-120)
intervalNoSupported chart timeframe: 5m, 15m, 1h, 4h, or 1d5m
token_idNoOptional 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.
analogueLimitNoNearest analogues used at every walk-forward anchor (5-200)
includePointsNoInclude every resolved walk-forward anchor. False returns an agent-sized summary

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description must disclose behavioral traits like safety (read-only vs. mutation) and side effects. It does not state whether the tool is read-only, what resources it accesses, or any rate limits. The only behavioral hint is the token_id parameter in the schema (payment required if omitted), which is not mentioned in the description. Thus, the description adds little transparency beyond the schema.

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 a single dense sentence that front-loads the primary purpose and includes the key outputs. It is concise and free of fluff, though it is packed with technical jargon. It earns its place but could be slightly more readable by breaking into two sentences.

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 tool has 9 parameters and no output schema, the description should explain return values. It lists several metrics (coverage, confidence intervals, Winkler scores, regime diagnostics), which gives a partial picture. However, it does not describe the overall structure of the response, error conditions, or the walk-forward methodology in detail. It is adequate but not complete for such a complex tool.

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?

Schema coverage is 100%, so the baseline is 3. The description does add some meaning by mentioning 'independent resolved anchors' and the key configuration parameters, but it does not enrich the understanding of parameters beyond what the schema already describes. It essentially repeats the parameter names without adding new semantic details.

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's function: it walks forward a candle configuration (symbol, interval, q, f) over resolved anchors and compares analogue/volatility-scaled bands against a baseline. It names the key parameters and specific analysis outputs (coverage, Winkler scores, regime diagnostics), distinguishing it from siblings like get_track_record or get_pattern_metrics.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention any exclusions, prerequisites, or scenarios for which this tool is appropriate. It simply describes what it does without contextualizing its use relative to sibling tools.

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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