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Get Portfolio Risk

get_portfolio_risk
Read-only

Risk analytics for a list of positions: per-position beta to BTC and ETH, sector concentration, pairwise correlation matrix, portfolio annualized volatility, 1-day 95% parametric VaR. Uses 14d of 1h Hyperliquid candles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
positionsYesArray of positions: { asset, side, notional_usd }. Max 20.

TDQS

A4.1/5.0
Behavior4/5

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

Annotations indicate readOnlyHint=true, and the description adds behavioral context by specifying the data source and time window (14d of 1h Hyperliquid candles). This goes beyond the basic annotation to inform the agent about computational scope and data freshness.

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?

Two sentences efficiently cover the tool's purpose, outputs, and data source with no extraneous information. Every phrase adds value.

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?

For a read-only tool with rich annotations and a well-described schema, the description fully explains what the tool computes and the data it uses. No output schema is needed as the listed outputs sufficiently inform the agent of the return structure.

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?

Schema coverage is 100% with detailed parameter descriptions, so the description adds minimal extra meaning about parameters. It contextualizes that the positions are used for risk computation, but does not elaborate on constraints or formatting beyond the schema.

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 computes risk analytics for a list of positions and enumerates specific outputs (beta, sector concentration, correlation matrix, volatility, VaR). This distinguishes it from sibling tools like get_market_context or get_macro_context which focus on broader market conditions.

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

Usage Guidelines3/5

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

The description implies usage when a user has a portfolio of positions and needs risk metrics, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among 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

A3.7/5.0
Disambiguation3/5

Many tools are specialized, but several pairs have fuzzy boundaries: e.g., get_funding_rates vs get_top_funding_rates, get_basic_macro vs get_macro_context, get_simple_iv vs get_options_iv. An agent could easily select the wrong one.

Naming Consistency4/5

Most tools follow a 'get_X' pattern with descriptive noun phrases. There are a few exceptions like 'create_api_key' and 'search_markets', but overall the convention is consistent and readable.

Tool Count2/5

With 47 tools, the server is overloaded. While the domain is broad, this many tools makes discovery and selection difficult for an agent, reducing coherence.

Completeness5/5

The tool set covers an impressively wide range: macro data, funding, prediction markets, OI history, whale tracking, risk analytics, position sizing, backtesting, and signal generation. It leaves no obvious gaps for a crypto trading assistant.