mcp

Support & Resistance Levels

get_levels

Read-only

[DRILL-DOWN] Support/resistance levels for a coin (e.g. 'BTC', 'ETH', 'SOL'). Reads from levels_engine + Hyperliquid mids. Levels are detected on THREE timeframes — 15m (3 days back), 4h (90 days) and 1d (2 years) — and every level carries its timeframe, distance_pct and zone band. nearest_resistance/nearest_support are the immediate intraday levels (within ~5% of spot) and are often fractions of a percent away; for swing structure read structure.resistance / structure.support, which are the 4h/1d levels out to 35% from spot, strongest first. A level merged across tiers lists the confirming timeframes in confirmed_by. Same data as REST /levels/{coin}.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`coin`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`coin`	Yes
`structure`	No
`timeframes`	No
`all_support`	No
`trend_state`	No
`current_price`	Yes
`all_resistance`	No
`nearest_support`	No
`nearest_resistance`	No

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description goes beyond this by revealing the data sources (levels_engine + Hyperliquid mids), the look-back windows, and the meaning of merged levels with confirmed_by. This adds valuable behavioral context without contradicting the annotations.

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 longer than average but every sentence adds meaningful information about timeframes, field semantics, and data sources. It is front-loaded with the tool's purpose and the [DRILL-DOWN] marker. Slight verbosity, but not wasteful.

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 output schema exists, the description does not need to restate return types. It explains the conceptual model (timeframes, distance_pct, nearest vs structure, confirmed_by) thoroughly enough for an agent to select and interpret results. It also notes parity with the REST endpoint, completing the context.

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 input schema has only one required parameter 'coin' with no description, so schema coverage is 0%. The description compensates by giving explicit coin format examples ('BTC', 'ETH', 'SOL') and explaining how the coin parameter drives the response. This is sufficient for a single, straightforward parameter.

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 retrieves support/resistance levels for a coin, specifies three timeframes, and gives concrete examples ('BTC', 'ETH', 'SOL'). It also differentiates the tool's scope by describing the data source and internal fields, distinguishing it from the many sibling get_* tools.

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?

The description provides clear context on when to use which part of the output: nearest_resistance/support for intraday levels and structure.resistance/support for swing levels. It does not explicitly name alternative tools or exclusions, but the [DRILL-DOWN] tag and detailed interpretation guidance give enough usage direction.

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

A4.1/5.0

Disambiguation4/5

Most tools have distinct purposes, but a few pairs like get_liquidation_map vs get_liquidity_map and multiple signal-fetching tools (get_signal, get_signals_since, list_signals, get_actionable_signals) could be confused without careful description reading. Overall, each tool has a clearly defined role, so the ambiguity is limited.

Naming Consistency4/5

The naming is overwhelmingly consistent: snake_case verb_noun patterns like get_price, list_signals, close_trade, and amend_trade. The exception is 'health' as a plain noun, and a few compound verbs like find_similar_signals, but the overall pattern is strictly followed.

Tool Count2/5

With 52 tools, this is far beyond the typical well-scoped range and falls into the 'too many' category. The broad platform scope justifies many tools, but the sheer number makes the set heavy and harder for an agent to navigate efficiently.

Completeness5/5

The surface covers the full lifecycle: journal management (log, amend, close, void, read), trade vetting (check, plan, rank), signal discovery (list, get, actionable, similar), market data (price, levels, indicators, options), context (macro, liquidity, flows), forensics (manipulation, trust, narrative), and meta (playbook, usage, health). No obvious gaps or dead ends.

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