kalshi_event_history

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

With no annotations, the description carries the burden. It describes output (candlesticks, grouped by market) but does not disclose potential behavioral traits like rate limits, data freshness, error handling, or pagination. The credential-free aspect is noted.

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

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

The description is a single sentence that efficiently conveys key information: what is returned, grouping, single event scope, and data source. It is concise without unnecessary words.

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

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

Given the tool has 6 parameters and no output schema, the description is adequate but not complete. It explains the basic output but does not cover return format, edge cases, or how to interpret the data. For a complex financial data tool, more context would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so the schema already explains parameters like event_ticker and period_interval. The description adds no new parameter details beyond the schema, meeting the baseline but not exceeding it.

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

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

The description clearly states the tool returns normalized Kalshi candlesticks grouped by market for one event, with specific context of being credential-free public data. This distinguishes it from sibling tools like kalshi_market_history and kalshi_historical_markets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool over alternatives. It mentions 'credential-free' which implies no authentication needed, but lacks guidance on when not to use it or specific trade-offs compared to other historical data tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.