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decker

by io.github.gigshow

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
NameRequiredDescriptionDefault
symbolYese.g. BTCUSDT
timeframeYes

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries the safety/behavior burden and does so thoroughly: it discloses zero recompute, null-on-absent semantics, the near-always-null trigger snapshot, the game.phase fallback, the conditional presence of object_context, and null behavior for KRX symbols and current_price.

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 first sentence is a clear front-loaded summary, and the rest is organized into labeled sections (DOMAIN FRAME, RAW CONTRACT, IMPORTANT, object_context). It is long, but the density of non-obvious behavior justifies the length for a tool with no annotations or output schema.

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 or annotations, the description maps the main returned blocks (game, action_gate, object_context, current_price) and explains freshness/null semantics and cross-timeframe behavior. It also closes the loop with a validate_intent call, making the tool safe to invoke.

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

Parameters4/5

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

The schema already provides an enum for timeframe and an example for symbol; the description adds non-obvious parameter-related context such as current_price being the latest across all timeframes rather than the requested tf, and symbol-universe exceptions for KRX individual stocks. This is enough to use the two required parameters correctly.

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 opening line names a specific verb ('get'), resource ('market state'), and scope ('symbol/timeframe'), and clarifies it returns persisted engine state with zero recompute. It also explicitly distances itself from human-language siblings (get_view/get_reading), so an agent can tell which tool to call.

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?

The description explicitly says to use decker.get_view or decker.get_reading for a human-language view, calls out that action_gate alone is only a posture, and directs agents to decker.validate_intent before placing orders. It thus provides both when-to-use and when-not-to-use guidance 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

A4.3/5.0
Disambiguation3/5

Execution and settings tools are clearly separated, but there is a dense cluster of analytical getters (get_view, get_reading, get_signals, get_assembly) that all return market verdicts and coordinates; their boundaries are only clear after reading the long descriptions. get_market_state versus get_state_timeline is cleaner, but the overlap among the analysis-verdict tools could still cause misselection.

Naming Consistency5/5

All tools share the decker_ prefix and a consistent snake_case verb_noun pattern (get_* for reads, place_order/close_position/update_protective_stops/set_skill_overlay/validate_intent for actions). There is no mixing of naming conventions or vague generic verbs.

Tool Count5/5

Thirteen tools is within the ideal well-scoped range for an execution-plus-analysis server. Each tool maps to a distinct responsibility (state reading, timeline history, signals, execution, position management, user settings, pre-trade validation), so none feels like filler.

Completeness4/5

The lifecycle is well covered: validate_intent → place_order → get_positions → update_protective_stops → close_position, with signal/analysis and skill-overlay tools around it. Minor gaps like a dedicated account-balance or full order-history tool are absent, but the execution engine handles caps server-side and closed round-trips are included in get_positions.

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