
Bring sports schedules, odds and supported fair-price analysis into an application or research agent.
- Access API · MCP
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An MCP server connects an assistant to documented research and data tools. Choose it by what the assistant can read or change, then inspect the upstream service and operating requirements.
Market lookup, order-book reads, positions and order actions are separate capabilities. A common MCP interface does not mean every listed venue supports every method. Check exchange coverage and response identifiers for the calls you need.
Enable trading actions only with deliberate authorization and an understood permission scope. A research connection should be evaluated on its own merits before adding signing capabilities. Compare SDKs when your application needs direct code integration rather than assistant-facing tools.

Bring sports schedules, odds and supported fair-price analysis into an application or research agent.

Compare sports prices and graded-wallet exposure, inspect unusual trades and connect the same research to an API.

Research markets conversationally, use supported order workflows and schedule wallet tasks with explicit access choices.

Compare sports prices and signal reasons in the app or query the same precomputed game research through MCP.

Compare resolution sources and clarity grades, then follow question-level links across Polymarket and Kalshi.

Retrieve historical prediction and options datasets for depth research, model testing and repeatable batch ingestion.

Structured settlement comparisons for covered Polymarket and Kalshi contract pairs.

Build wallet groups, inspect copyability and receive cohort alerts in-app or through Telegram.

Use shared Python market and order methods across venues, with optional local or hosted MCP access.

A normalized prediction-market data service for dashboards, historical replay and AI clients.

Source-linked event reports, model-price comparisons and developer access through API, MCP and CLI.

Practice market selection and position accounting with virtual funds, a journal and optional assistant-driven paper orders.