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Get Outcome Edges

get_outcome_edges
Read-only

One-call mispricing scanner for autonomous agents: every live HIP-4 PRICE market, each priced against its options/vol-implied fair value and ranked by |edge|. BTC/ETH use the Deribit IV smile; other assets use Hyperliquid realized vol (iv_source labels which). edge_pt = (implied_prob - yes_price)*100, computed only where a real traded YES price exists (synthetic/untraded 0.5 placeholders return edge_pt:null so no fake edge is surfaced). Filter with min_edge_pt / underlying. The whole board without looping.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax markets to return (default 30, max 100).
underlyingNoFilter to one underlying ticker, e.g. "BTC".
min_edge_ptNoOnly return markets whose |edge| in probability points is at least this (default 0 = the whole board).

TDQS

A4.2/5.0
Behavior4/5

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

With readOnlyHint and openWorldHint annotations, the description adds value by detailing the edge_pt calculation (implied_prob - yes_price)*100 and the null handling for synthetic/untraded placeholders, which prevents misleading edges. It also clarifies IV source selection (Deribit vs realized vol) and the labeling via iv_source. No contradiction with annotations.

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 efficient, with every sentence contributing value: purpose, calculation method, null behavior, and filtering options. It is front-loaded with the main purpose and avoids unnecessary fluff, making it highly readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite lacking an output schema, the description explains the core computation and edge cases (null for untraded placeholders) effectively. It covers the essential behavior for an autonomous agent to decide and use the tool, though it does not enumerate all possible return fields.

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 description coverage is 100%, so parameters are fully described there. The description adds minimal new meaning beyond restating that filtering can be done with min_edge_pt and underlying. It does not introduce additional semantic context beyond what the schema already provides.

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's purpose: a one-call mispricing scanner for live HIP-4 PRICE markets, ranking them by |edge|. It distinguishes itself from siblings by specifying the unique function (pricing against implied fair value) and the scope (all live markets, not filtered). The verb 'scanner' and resource 'live HIP-4 PRICE markets' are specific.

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 gives context on when to use it: for autonomous agents needing a mispricing scan across the whole board without looping, and suggests filtering via 'min_edge_pt / underlying'. It does not explicitly name alternatives or state when not to use it, but the distinct purpose and phrasing imply its unique role 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.