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kalshi_event_history

Retrieve candlestick price, bid/ask, volume, and open interest data for Kalshi event markets by event ticker and time range.

Instructions

Kalshi event history. Returns candlesticks for the markets in a Kalshi event, grouped by market, for event_ticker. start_ts defaults to 7 days ago, end_ts to now, period_interval to 1440 minutes, and series_ticker to the ticker prefix before the last dash. Each candle has price, yes bid and ask, volume and open interest. Ranges are capped at 366 days.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "event_ticker": "<event_ticker>"
    },
    "name": "kalshi_event_history"
  }
}

Input Schema

Name	Required	Description
`end_ts`	No	Unix end timestamp in seconds. Defaults to now.
`start_ts`	No	Unix start timestamp in seconds. Defaults to 7 days ago.
`event_ticker`	Yes	Kalshi event ticker
`series_ticker`	No	Kalshi series ticker. Defaults to the event ticker prefix before the last dash.
`period_interval`	No	Candlestick interval in minutes. Default: 1440.
`include_latest_before_start`	No	Include the latest candle before start_ts when supported upstream.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.1.0Jun 12, 2026

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden well: it discloses the returned candle fields, grouping by market, default time window, and the 366-day range cap. It does not cover authentication, pagination, or ordering guarantees, but it gives a solid picture of what to expect.

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 a single compact paragraph that leads with the core purpose and then layers in output shape, defaults, and constraints. Every sentence contributes useful information without waste.

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 six-parameter historical data tool with no output schema and no annotations, the description is largely complete: it explains the return format, grouping, defaults, and range cap. The main remaining gap is the lack of guidance on alternative history tools.

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 the schema already documents all six parameters including defaults. The description reinforces several defaults and the range cap, but adds little beyond the schema for parameters like include_latest_before_start.

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 candlestick history for the markets within a Kalshi event, grouped by market. It is specific about the resource and output shape, though it does not explicitly contrast itself with closely related siblings like kalshi_market_history.

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?

Usage is implied by the historical candlestick framing and the event_ticker requirement, but there is no explicit guidance on when to use this tool versus alternatives such as kalshi_market_history or kalshi_historical_market_history. The defaults and 366-day cap help but do not fully route the agent.

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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