skipseek

Get one market, venue by venue

get_market_probabilities

Read-only

The full current picture for ONE market: the SkipSeek Fair Probability, every venue pricing it with that venue's own price snapshot and how closely its contract matches the standardized question, each venue's signed Probability Gap against fair value (labelled Market Premium, Market Discount, In Line or Cross-Market Discrepancy), and the latest confidence score and band. USE WHEN you have a slug and need where the price stands now, or to answer "which venue is out of line and by how much". USE SOMETHING ELSE WHEN you do not have a slug yet (list_markets), when you need how the price MOVED over time (get_market_history), or when you want the written analysis and its cited sources (get_market_research). Read-only. Probabilities are 0–1; gaps are in percentage points. An unknown slug returns a plain not-found message, not an error.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`market`	Yes	Market slug, exactly as returned in the `slug` field by list_markets, list_discrepancies or list_movers (e.g. "fed-cuts-rates-by-september"). Opaque — do not construct or guess one; a slug that does not exist returns a not-found message rather than data.

Output Schema

TableJSON Schema

Name	Required	Description
`gaps`	No
`tier`	No
`market`	No
`notice`	No	Set INSTEAD of data when the call succeeded but has nothing to return: unknown slug, empty result, a tool not on this tier, or quota spent. Not an error.
`source`	No
`sources`	No
`confidence`	No
`attribution`	No
`generated_at`	No
`fair_probability`	No	Decimal 0-1.
`requests_remaining_today`	No

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint and openWorldHint, but the description adds significant behavioral context: probabilities are 0–1, gaps are in percentage points, and an unknown slug returns a not-found message rather than an error. It also explains the meaning of labels like Market Premium/Discount. This goes well beyond the annotations and sets accurate expectations.

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 organized with a clear first sentence enumerating outputs, followed by explicit USE WHEN/USE SOMETHING ELSE blocks. Though the opening sentence is dense and packs many clauses, every part earns its place and the structure makes scanning easy.

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 a single parameter, an output schema, and strong annotations, the description is fully complete: it covers purpose, usage conditions, alternatives, units, error behavior, and output labels. No important context is missing.

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% and the schema's 'market' description already details slug format, opacity, and not-found behavior. The tool description repeats this guidance but adds no new parameter-level semantics beyond reinforcing 'when you have a slug'. Baseline 3 is appropriate since the schema does the heavy lifting.

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 states a specific verb and resource: 'the full current picture for ONE market' with detailed output components (Fair Probability, venue pricing, Probability Gap labels). It distinguishes from siblings by explicitly naming list_markets, get_market_history, and get_market_research as alternatives, so the agent knows exactly what this tool uniquely provides.

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 gives explicit usage rules: 'USE WHEN you have a slug and need where the price stands now' and lists concrete alternatives with when to use them (list_markets without a slug, get_market_history for movement, get_market_research for written analysis). This fully guides tool selection.

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

A4.7/5.0

Disambiguation5/5

Every tool targets a distinct resource and action: create_forecast is the only write tool, get_* reads single entities (market probabilities, history, research, forecaster record), and list_* scans collections (markets, movers, discrepancies, platforms). No two tools overlap in purpose, and the three market reads are clearly separated as current vs. historical vs. qualitative.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: get_ for single-item reads, list_ for collection reads, and create_ for the sole write operation. There are no mixed conventions or vague verbs.

Tool Count5/5

9 tools is well-scoped for a prediction-market server: one deliberate write action, four single-market/forecaster reads, and four collection-browsing tools. Nothing feels redundant, and the count is right in the sweet spot for the domain.

Completeness4/5

The server covers the core workflow: discover markets (list_markets), analyze current price/history/research (get_market_*), detect cross-venue disagreement and movement (list_discrepancies, list_movers, list_platforms), and commit a forecast (create_forecast) with auditing via get_forecaster_record. A minor gap is the lack of a tool to fetch a single forecast's details after submission, though the returned URL and aggregate record mitigate this.

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