# parlay-mcp

> parlay-mcp — parlay-run-parlay-mcp. Use this tool when you need to aggregate and compare predictions across multiple platforms, solving the problem of fragmented market data. It takes in search queries and market selections as inputs, and outputs comparative data from Polymarket, Kalshi, Limitless, and Manifold. Use parlay-mcp to inform decision-making and stay up-to-date on market trends by consolidating information from various prediction markets.

Canonical page: https://skillsregistry.net/skills/parlay-run-parlay-mcp  
JSON: https://api.skillsregistry.net/v1/skills/parlay-run-parlay-mcp

## Description

MCP server for prediction markets — search and compare across Polymarket, Kalshi, Limitless, Manifold

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** search
- **Updated:** 2026-09-22

## Source

- **Source listing:** [GitHub](https://github.com/parlay-run/parlay-mcp)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "parlay-run-parlay-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/parlay-run-parlay-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/parlay-run-parlay-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt