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Get HIP-4 vs PM Arb

get_hip4_vs_pm_arb
Read-only

Finds the same underlying market priced on both HIP-4 (on-chain Hyperliquid) and Polymarket, flagging spreads above threshold. A spread means one venue is mispriced relative to the other.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
min_spread_pctNoMinimum spread between HIP-4 and Polymarket YES prices to flag (percentage points, default: 3)

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, establishing safety. The description adds behavioral context by explaining that it flags spreads above a threshold and that a spread indicates mispricing. No contradictions.

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, no redundancy. The first sentence states the purpose and action, the second explains the concept of spread. Every word is necessary and informative.

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 simplicity (one optional parameter, no output schema, annotations present), the description covers the tool's functionality, the input's role, and the interpretation of results. It is fully adequate for an agent to select and use the tool correctly.

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 a detailed description for the single parameter (min_spread_pct) including default, range, and meaning. The tool description does not add further parameter semantics beyond what the schema provides, so baseline 3 is appropriate.

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 a specific action ('finds the same underlying market priced on both HIP-4 and Polymarket') with a defined outcome ('flagging spreads above threshold'). It is distinguishable from sibling tools like 'get_pm_hl_divergences' by focusing on HIP-4 vs Polymarket specifically.

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 explains the context of use (finding mispricing between two venues) and defines what a spread means. It does not explicitly state when not to use or list alternatives, but the purpose is sufficiently clear for selection among siblings.

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.