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kalshi_markets

Fetch normalized Kalshi market rows from public data. Filter by status, ticker, event, or series, and paginate results for easy market analysis.

Instructions

Kalshi markets. Returns normalized Kalshi market rows from credential-free public market-data JSON. The status enum accepts unopened, open, closed, and settled.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return, default 25, max 200
cursorNoPagination cursor from a previous Kalshi response
statusNoMarket status filter
tickerNoKalshi market ticker filter
event_tickerNoKalshi event ticker filter
series_tickerNoKalshi series ticker filter

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / status / enum
      Added value: +[
      +  "unopened",
      +  "open",
      +  "closed",
      +  "settled"
      +]
  2. Addedv1.6.0
  3. Removedv1.6.0
  4. Addedv1.1.0

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description does carry some burden and does disclose useful behavioral context: data comes from credential-free public market-data JSON and rows are normalized. It does not explain result ordering, pagination behavior, or whether resolved/current markets are both included, but for a simple listing tool this is an acceptable partial disclosure.

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 short and readable, with the core 'returns normalized rows' statement front-loaded. The opening sentence 'Kalshi markets.' is somewhat redundant with the tool name, but it is harmless and the overall description stays tight.

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

Completeness2/5

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

The tool has six optional filters, no output schema, and many Kalshi sibling tools, but the description gives only a vague sense of the returned payload ('normalized Kalshi market rows'). It does not explain result shape, how filters interact, or when to choose this over related Kalshi endpoints, leaving an agent under-informed.

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?

The input schema describes all six parameters, giving 100% schema coverage, so the baseline is 3. The description adds no real parameter semantics beyond restating the status enum values that already exist in the schema.

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 uses a specific verb and resource: 'Returns normalized Kalshi market rows from credential-free public market-data JSON.' The status enum adds useful functional scope. However, it does not explicitly distinguish this tool from sibling Kalshi endpoints like kalshi_events or kalshi_markets_history, so it is clear but not fully differentiated.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives. The phrase 'credential-free public market-data JSON' implies no authentication is needed, but the description never says when to prefer this over sibling tools such as kalshi_historical_markets or kalshi_market_orderbook.

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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