Decker

decker.get_market_state

Market State v0 — current engine structural state for a symbol/timeframe (latest evaluated bar, persisted engine emit read as-is, zero recompute). DOMAIN FRAME (why this engine exists): the market is read as a TARGET GAME — every coordinate comes from a verified anchor (a past level where a triggered move actually succeeded). The game block tells you the context that matters: game.status = forming_target (new anchor set, awaiting test) | testing_target (price is testing whether the declared target holds) | direction_resolved (game decided, price traveling); game.target = WHO is being judged (anchor id/phase/band); game.progress_dest = where price goes if the move proceeds (the opposing verified anchor to conquer); game.reverse_dest = where it goes if the move fails (the opposite house — also the stop logic's home); game.why_gate = full gate derivation chain; game.zt_regime = output canonicality (restored = deterministic delta lineage). action_gate alone (GO/WATCH/HOLD) is only a posture — the game context is the information. RAW CONTRACT: fields are engine-native vocabulary (c_state, hold_reason, R_* risk enums …), NOT customer-facing prose — for a human-language view use decker.get_view (with tf) or decker.get_reading. layer=STATE: this is a market-state reading, NOT a trade instruction. Absent fields are null (engine did not emit that axis — no filling). IMPORTANT: top-level state.c_state/action_gate/trigger_kind reflect the TRIGGER SNAPSHOT (only populated on a bar that actually had a trigger event) — null on most bars is normal, not a data gap. For the always-present, every-bar-populated view of the same axes use game.phase.c_state / game.phase.action_gate instead (different freshness, same underlying engine state machine). Don't read a null top-level field as 'engine has no state' — check game.phase first. object_context (top-level, W1-C1 standard object block, present when a recent trigger bar exists): my_anchor/opp_anchor(reversal destination)/judgment_ref/geometry/why(engine reason_codes)/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). current_price (top-level, 2026-09-03): {price, bar_ts} — the single latest completed-bar close for this symbol ACROSS ALL timeframes (not just the requested tf), useful when comparing multiple timeframes' target bands against one 'now' price. null for KRX individual-stock symbols. Before placing any order through any execution tool, check the intent with decker.validate_intent.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	Yes	e.g. BTCUSDT
`timeframe`	Yes

Schema Changelog

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

First observedJul 12, 2026

TDQS

A4.1/5.0

Behavior5/5

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

With no annotations, the description carries the full burden, and it excels: zero recompute, read-as-is persisted emit, null semantics explained as normal rather than data gaps, KRX stock exceptions, and cross-timeframe current_price behavior. It also discloses the object_context conflict condition and warns about validate_intent before ordering.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening sentence is front-loaded and gives a good summary, but the description is very long and dense, with a large DOMAIN FRAME digression and heavily nested parentheticals. The details are valuable, but the structure could be tighter without losing meaning.

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?

Without an output schema, the description thoroughly documents the important output blocks, field semantics, null behavior, freshness differences, and cross-references to validate_intent. This is about as complete as a description of a complex state-tool can reasonably be.

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 symbol with an example and timeframe via enum, and the description does not add significant parameter-level meaning. It refers to 'requested tf' and symbol context, but the input parameters are simple enough that the schema plus a small reference is nearly adequate.

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 clearly states this is a read-only retrieval of 'current engine structural state for a symbol/timeframe' with a specific verb and resource. It does not explicitly name sibling tools to differentiate, but the 'persisted engine emit read as-is, zero recompute' framing helps separate it from signal/reading getters.

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?

It provides solid context for when fields are populated (trigger bars, non-trigger bars, KRX exclusions), and explicitly says to use game.phase for always-present state and to check validate_intent before orders. However, it does not directly say when to use get_market_state versus get_reading/get_signals/get_assembly.

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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