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Decker

by com.decker-ai.api

decker.update_protective_stops

Axis③ (Order/Execution) — modifies the stop-loss and/or take-profit on an EXISTING open position. There is no cancel_order tool because Decker only places market orders (there is no resting order to cancel) — the actual gap this fills is modifying protective stops on a position you already hold. real: cancels the old exchange stop/take-profit order(s) and places new ones at the given price(s) (new order placed first, old one canceled only after — no unprotected window). virtual: updates the paper position's stop_loss/take_profit columns directly (polled by the paper monitor). Provide at least one of sl_price/tp_price — the other side, if omitted, is left at its current value. If both a real and a virtual position are open for this symbol, pass mode explicitly or the call is rejected asking which one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoOnly required when both a real and a virtual position are open for this symbol — says which one to modify.
symbolYese.g. BTCUSDT — must be a symbol you currently hold.
sl_priceNoNew stop-loss price. Omit to leave the current stop unchanged.
tp_priceNoNew take-profit price. Omit to leave the current target unchanged.

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description carries full burden and excels: it details real-mode ordering (new order first, old canceled after, no unprotected window), virtual-mode direct column updates, and the rejection behavior for ambiguous mode.

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 dense but every sentence provides essential information: purpose, real vs virtual behavior, parameter rules, and mode requirement. It is front-loaded with the main purpose and avoids redundancy, making the length justified.

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?

Given the tool's complexity (real/virtual modes, order of operations, dual-position ambiguity), the description covers all critical aspects a caller must know. It is self-sufficient for an agent to invoke correctly without needing output schema details.

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?

Though schema coverage is 100%, the description adds crucial semantics: the 'at least one' constraint, that omitted side is left unchanged, the symbol must be currently held, and mode is required only in dual-position scenarios—all beyond the schema's simple field descriptions.

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 clearly states the tool 'modifies the stop-loss and/or take-profit on an EXISTING open position', using a specific verb and resource. It also distinguishes itself from siblings by explaining why there is no cancel_order tool and that this fills the gap of modifying protective stops.

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 this is for modifying stops on an existing position, contrasts with market-order placement, and provides concrete rules: provide at least one of sl_price/tp_price, and pass mode when both real and virtual positions exist, otherwise the call is rejected.

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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