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Wicked MCP server

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by WickedAPI
OverviewSchemaSourceRelated ServersDiscussions(1)Admin
TypeScript
Hybrid

kalshi_probability

Read-onlyIdempotent

Fetch live Kalshi prediction-market probabilities by category or specific ticker. Returns the most active open markets for a category, free and read-only.

Instructions

[Trading Data API] Free, no signup required. Kalshi prediction-market probabilities. Query by category (returns the most active open markets) or a specific market ticker.

Example: /v1/kalshi-probability?category=crypto

Use when you want live Kalshi prediction-market probabilities by category or ticker. Not for a single market's depth (use kalshi_market) or Polymarket (use polymarket).

Behavior: read-only and free; no key or payment needed.

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": {
      "category": "<category>"
    },
    "name": "kalshi_probability"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoOptional. Max markets to return for a category query (default 25, max 100).
tickerNoOptional. A specific Kalshi market ticker (e.g. KXBTCD-...).
categoryYesOptional. One of: crypto, macro, fed-rate, financials, politics, sports, world, companies, climate, science, health, entertainment, elections. Either category or ticker is required.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds non-annotation context: free, no signup, no key or payment, and that a category query returns the most active open markets. It stops short of describing pagination or the shape of the returned probability data, which keeps it off 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.

Conciseness5/5

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

Front-loaded purpose, then example, then routing guidance, then behavior — each sentence earns its place. No filler, and the exclusions are placed before the behavioral footnote where an agent will actually read them.

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 read-only public data tool with full schema coverage and no output schema, the description covers purpose, routing, cost and the one return-value nuance that matters (most active open markets). The remaining gap is that nothing tells the agent what the probability payload looks like or about the category-required schema quirk.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3, but the description adds meaning beyond the schema: it explains the semantics of a category query (returns the most active open markets rather than an arbitrary slice) and gives a worked example URL. It does not resolve the schema's internal conflict where 'category' is listed as required while its own description says category OR ticker is required.

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?

States a concrete verb+resource ("Kalshi prediction-market probabilities") plus the two query modes (category or ticker), and explicitly names the sibling tools it is not (kalshi_market, polymarket). An agent can distinguish it from all ~80 siblings without opening the schema.

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?

Explicit when-to-use ("Use when you want live Kalshi prediction-market probabilities by category or ticker") plus two named exclusions with the correct alternative for each (kalshi_market for single-market depth, polymarket for Polymarket). That is the full when/when-not/alternatives pattern.

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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