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Prediction Market Rule Check MCP

kalshi_weather_settlements

PAID $0.02/call (x402) or subscription / bundle pass. Settled Kalshi weather markets for a series (e.g. KXHIGHNY) or city: per market the ticker, close date, exact close_time (ISO 8601 UTC, Z) and close_time_local (ISO with the city's UTC offset; local_tz IANA zone, null if unknown), settlement_time (Kalshi settlement timestamp, ISO UTC; null if Kalshi gives none) and close_to_settlement_seconds / close_to_settlement_minutes (gap from close to settlement, to spot late settlements or revisions), expiration_time, strike/bucket, yes/no result and settlement payout, plus the observed settlement value per day and the official settlement source. Optional date range. Information only, not betting advice. Historical settlements do not predict future results. 18+. BeGambleAware.org

Input Schema

NameRequiredDescriptionDefault
toNoClose date to, YYYY-MM-DD
cityNoAlternative to series: city/station keyword (e.g. London, Chicago)
fromNoClose date from, YYYY-MM-DD
kindNoWith city: which series type (default high)
limitNoMax markets, default 100, max 500
seriesNoSeries ticker, e.g. KXHIGHNY, KXHIGHTEGLL, KXRAINNYC
access_tokenNoSubscriber token from /success (or Authorization: Bearer)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations, the description carries the burden well: it discloses the paid x402/subscription model with a concrete price, the auth path (token from /success or Bearer), the fact that values can be null when Kalshi provides none, and the exact fields returned. It does not explicitly say the operation is read-only or discuss rate limits beyond pricing, so it stops short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is front-loaded with pricing and the core purpose, but the body is one very long run-on sentence that enumerates many output fields, making it dense and hard to scan. The legal boilerplate (18+, BeGambleAware) is likely required but consumes space without helping tool selection.

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?

For a 7-parameter tool with no output schema and no annotations, the description compensates well by enumerating the returned fields (ticker, close times, settlement time, gaps, strike, payout, observed value, source) and covering auth and pricing. It is nearly complete, missing only an explicit note on read-only behavior or return ordering/pagination.

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%, so every parameter is already documented in the schema, including the series-vs-city alternative and the kind enum. The description reinforces the series/city duality and optional date range but adds no format, default, or constraint detail beyond what the schema states. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns settled Kalshi weather markets for a series or city, naming concrete examples (KXHIGHNY). It is distinguishable from a live-markets tool by the word 'settled', but it never explicitly contrasts itself with the sibling kalshi_weather_series, leaving that differentiation to inference.

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?

It implies usage ('settled', 'historical settlements') and says the date range is optional, so an agent can infer this is the historical lookup. However, it never states when to prefer this over kalshi_weather_series or live_markets, nor any exclusions or prerequisites beyond the access_token mention.

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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