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candlestick_signals

Read-onlyIdempotent

Detect classic candlestick patterns on a ticker's recent daily bars: hammer, inverted hammer, bullish/bearish engulfing, doji, morning/evening star, and shooting star. Returns each detected pattern with {pattern, date, direction (bullish/bearish/neutral), barIndex}. HEURISTIC pattern detection with conservative default thresholds; for research, not investment advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesUS ticker (e.g. 'MSFT').
lookback_daysNoTrailing daily bars to scan for patterns (default 60).

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already establish readOnly/idempotent/non-destructive, so the bar for this dimension is lower. The description adds valuable behavioral context: detection is 'HEURISTIC' with 'conservative default thresholds', and returns are structured with pattern/date/direction/barIndex. This goes beyond the annotation safety profile, though it does not cover error behaviors or data source specifics.

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 two sentences with no filler; it packs pattern list, return shape, and a caveat. Every clause earns its place and the core purpose is front-loaded.

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

Completeness4/5

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

Given the simplicity (2 params, no output schema), the description covers the essential return contract by listing pattern, date, direction, and barIndex. It also flags heuristic behavior and research-only purpose, so an agent has enough to decide whether this tool fits. Minor omissions like empty-result behavior and exact barIndex definition prevent a 5.

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 description coverage is 100%, so the schema fully documents symbol and lookback_days. The description adds no parameter-level meaning beyond what the schema already provides; the mention of 'recent daily bars' simply echoes the trailing-days concept in the schema.

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 specifies a clear verb ('Detect'), resource ('candlestick patterns on a ticker's recent daily bars'), and enumerates the exact patterns detected. It is specific, but does not explicitly compare itself to sibling tools like stock_history or support_resistance_levels, so it misses the sibling-differentiation element for a 5.

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

Usage Guidelines3/5

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

The description implies its use for candlestick pattern detection and includes a research-only caveat, but it does not provide explicit when-to-use/when-not-to-use guidance or name alternative tools for related analysis (e.g., stock_history for raw bars, support_resistance_levels for levels). The 'for research, not investment advice' note is a limitation, not a routing instruction.

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

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

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