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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).

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover the read-only, idempotent, non-destructive nature, lowering the bar. The description adds important behavioral context by flagging HEURISTIC detection with conservative default thresholds and stating the tool is for research, not investment advice. This helps the agent temper expectations about precision.

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?

Two tightly written sentences: the first delivers the core action, pattern list, and return shape; the second adds a necessary heuristic caveat. No filler, and the most important identifying information 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?

With no output schema, the description's explicit return-field list ({pattern, date, direction, barIndex}) is valuable. It covers the essentials for a simple read-only scanner, though it could mention edge cases or the meaning of barIndex more explicitly. Overall, it is sufficiently complete for the tool's complexity.

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 already documents symbol and lookback_days. The description does not add substantial parameter-level detail beyond reaffirming the daily-bar scope, so the baseline of 3 is appropriate.

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 states a specific action ('Detect'), a specific resource ('classic candlestick patterns on a ticker's recent daily bars'), and enumerates the exact patterns covered. It also specifies the return fields, making the tool's purpose unambiguous and clearly distinct from siblings like stock_history or support_resistance_levels.

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 usage context (recent daily bars, research-oriented) and adds a 'not investment advice' caveat, but it does not explicitly say when to prefer this tool over similar technical-analysis siblings like support_resistance_levels or bounce_scanner. No alternatives or exclusions are named.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.