Decker

decker.get_signals

Active trading signals for the current user (with Skill Overlay applied), in customer-facing shape: coordinates (entry/target/stop), decision (ENTER/WAIT/SKIP — is_actual_trigger=true only for ENTER; WAIT/SKIP are standing candidates, not executed triggers), action_gate posture (GO/WATCH/HOLD — a stance, not an order command), progress, MTF verdict, and a plain-language summary_ko line. risk_reward_ratio is computed on the DISPLAYED coordinates (after overlay). Signals are retained rather than cut when they age (turn-retention policy) — read freshness_state (open|aged) / age_bars / freshness_sec before treating an old PENDING row as current. Filtered by symbols / min_progress / action_gate — action_gate here filters the CURRENT-MOMENT representative state, it does not search history (almost always 0 rows unless a gate is GO right now); for past GO events with their entry/target/stop and realized performance use decker.get_trigger_history instead. object_context (W1-C1 standard object block, present when a recent trigger bar exists): my_anchor / opp_anchor (reversal destination) / judgment_ref / geometry / why (action_gate + trigger_kind only — internal reason codes are scrubbed on this customer surface, use decker.get_market_state for those) / reverse_branch context (object_context.reverse_direction_conflict is present only when a local reversal shows stage='confirmed' but the swing's confirmed direction still disagrees — read it before treating reverse_branch.stage='confirmed' as a swing-level reversal). null on non-trigger bars or symbols outside the narrative universe (e.g. individual KRX stocks). Before placing any order through any execution tool, check the intent with decker.validate_intent.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No
`symbols`	No	Symbol filter (e.g. ['BTCUSDT','ETHUSDT']). Omit for all.
`timeframe`	No	Signal horizon filter (30m=scalp, 1h=swing, 4h/8h/1d=position). The same symbol can hold OPPOSITE directions on different horizons — omit to get the latest active signal regardless of horizon (its timeframe field says which one you got; when a specific symbols[] was requested, a row's other_horizon_conflict field flags it if another horizon is ACTIVE with the opposite direction). Prefer decker.get_assembly for the composed cross-horizon judgment instead of guessing which horizon to pass here.
`action_gate`	No	Engine action gate filter (3-layer grammar: gate = transition posture, not an order command). Rows where the engine emitted no gate for this bar (effective_action_gate null, e.g. KRX daily) are excluded when this filter is set.
`min_progress`	No	Minimum progress_pct (0-100).

TDQS

A4.9/5.0

Behavior5/5

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

With no annotations, the description carries full behavioral burden and delivers: decision semantics (WAIT/SKIP standing candidates, not executed triggers), action_gate as a stance rather than order command, risk_reward computed on displayed post-overlay coordinates, turn-retention policy with freshness_state/age_bars/freshness_sec, and null behavior for non-trigger bars or symbols outside the narrative universe. It even flags the reverse_branch nuance before treating stage='confirmed' as swing-level.

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 extremely dense and front-loaded with the core purpose, and every sentence earns its place for such a complex tool. However, it is a single long paragraph with many caveats, so it could be improved with breaks or bullet-like separation for faster agent scanning.

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

Completeness5/5

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

Despite no output schema and no annotations, the description is sufficient to call the tool correctly: it lists the returned field groups, explains freshness/retention, defines null cases, flags cross-horizon conflicts, and points to the right sibling tools for missing internal details. An agent has enough context to decide when to invoke it and interpret results.

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

Parameters5/5

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

While schema description coverage is already high (80%), the description adds material meaning beyond the schema: action_gate filters current representative state and excludes rows with no gate; timeframe rows can hold opposite directions with other_horizon_conflict; and symbols outside the narrative universe are null. It clarifies semantics that raw enum/schema cannot convey.

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 resource ('Active trading signals for the current user') with Skill Overlay and a precise output shape (coordinates, decision, action_gate, progress, MTF verdict, summary_ko). It also differentiates from siblings by explicitly routing historical GO events to get_trigger_history, internal reason codes to get_market_state, and cross-horizon judgment to get_assembly.

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

Usage Guidelines5/5

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

It gives explicit when/when-not guidance: action_gate filters the current-moment representative state, not history, and past GO events should use get_trigger_history. It also says to prefer get_assembly instead of guessing a timeframe, and to check decker.validate_intent before placing orders.

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

A4/5.0

Disambiguation5/5

Each tool targets a distinct purpose: state readings (raw vs. view vs. AI-synthesized), signals vs. historical triggers, execution (place/close/update stops), pre-trade validation, and skill management. Cross-references between tools (e.g., get_signals vs. get_trigger_history) explicitly clarify boundaries, leaving no ambiguity about which tool to call.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with the 'decker.' prefix: get_* for reads, place/close/update for actions, set_* for settings, and validate_* for checks. This uniformity makes the tool surface predictable and easy to navigate.

Tool Count5/5

14 tools is well-scoped for a comprehensive trading engine MCP, covering state observation, signal generation, execution, risk management, and user configuration. Each tool earns its place and there are no redundant or missing core functions.

Completeness5/5

The tool surface covers the full trading lifecycle: reading market state (multiple layers), obtaining signals and historical triggers, checking positions, opening/closing positions, updating protective stops, validating intent before orders, and managing skill overlays. The absence of a cancel_order tool is explicitly justified (only market orders), and the domain shows no obvious gaps.

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