MCP for Prediction Markets — AI-Powered Trading Research with Claude Code and Cursor

# Install in one command
uvx rekko-mcp

What this page covers

What MCP is and why it matters for prediction market research
Installing and configuring rekko-mcp for Claude Code and Cursor
Workflow examples: screening, analysis, arbitrage, portfolio
Available MCP tools (25+)
When to use MCP vs the REST API

What is MCP?

The Model Context Protocol (MCP) lets AI coding assistants call external tools during a conversation. Instead of copy-pasting API calls, you describe what you want in natural language and the assistant uses the right API endpoints automatically. Rekko’s MCP server (rekko-mcp) exposes 25+ tools covering market data, deep analysis, trading signals, arbitrage detection, and portfolio management. Your AI assistant calls these tools behind the scenes while you focus on the research question.

Installation

Requirements

Python 3.10+
A Rekko API key (sign up free)

Claude Code

claude plugin install rekko

{
  "mcpServers": {
    "rekko": {
      "command": "uvx",
      "args": ["rekko-mcp"],
      "env": {
        "REKKO_API_KEY": "rk_free_your_key_here"
      }
    }
  }
}

Cursor

Create or edit .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "rekko": {
      "command": "uvx",
      "args": ["rekko-mcp"],
      "env": {
        "REKKO_API_KEY": "rk_free_your_key_here"
      }
    }
  }
}

Verify installation

Ask your assistant: “List the top 5 prediction markets by volume.” If rekko-mcp is configured correctly, it will call the list_markets tool and return formatted results.

Workflow examples

1. Market screening

You say: “Find Kalshi markets with high volume that might have mispriced odds.” What happens behind the scenes:

Assistant calls list_markets(source="kalshi", limit=20) to fetch active markets
Calls screen_markets(...) to score them by volume, movement, and category
Presents the top candidates with scores and recommended actions

Example output:

Top Kalshi markets by opportunity score:

1. KXFED-26APR30 — "Fed rate decision April 2026"
   Score: 82 | Volume: $1.2M | Action: analyze

2. KXBITCOIN-26APR — "Bitcoin above $150K by April?"
   Score: 74 | Volume: $890K | Action: analyze

3. KXINFLATION-26MAY — "CPI above 2.5% in May?"
   Score: 68 | Volume: $540K | Action: watch

2. Deep analysis

You say: “Analyze the Fed rate decision market on Kalshi. I want to understand the key factors driving the price.” What happens:

Calls analyze_market(bet_text="Fed rate decision April 2026", platform="kalshi")
Returns an analysis_id — the research pipeline takes 30-90 seconds
Polls check_analysis_status(analysis_id) until complete
Retrieves the full analysis with probability, confidence, edge, key factors, and risks

Example output:

Analysis complete for KXFED-26APR30:

Probability: 71% (market: 62%) — Edge: +9 points
Confidence: 82% | Risk: low
Recommendation: BUY YES

Key factors:
- PCE inflation at 2.1% — within Fed's comfort zone
- Three FOMC members signaled openness to cuts
- Jobless claims trending upward (225K → 238K)

Risks:
- Strong employment report could reverse dovish signals
- Tariff escalation could reignite inflation expectations

Sources analyzed: 14

3. Arbitrage hunting

You say: “Are there any arbitrage opportunities between Kalshi and Polymarket right now?” What happens:

Calls get_arbitrage(min_spread=0.02) for cached results (instant)
If you ask for fresh data, calls get_arbitrage_live(min_spread=0.02) (10-30 seconds)
Returns matched events with spreads, scores, and which platform is cheaper

4. Portfolio management

You say: “Show my shadow portfolio and suggest what to do next.” What happens:

Calls get_portfolio(mode="shadow") to list open positions
Calls get_performance(mode="shadow") for aggregate stats
May call check_resolutions() to settle any resolved markets
Presents positions, P&L, win rate, and suggestions

5. Trading signals

You say: “Should I buy the Bitcoin

150K market? I have a

10K bankroll and already hold positions in the Fed rate market.” What happens:

Calls get_portfolio_strategy(...) with your current positions and bankroll
Returns a portfolio-aware signal accounting for correlation between positions
Includes Kelly-optimal size, hedge recommendations, and concentration warnings

6. Market data refresh

You say: “Refresh the Kalshi market data.” What happens:

Calls run_scraper(platform="kalshi") to pull the latest markets
Fresh data is available for subsequent queries

Available tools

Market intelligence

Tool	Description
`list_markets`	Browse active markets, filter by platform
`get_market`	Get a single market with pricing data
`search_markets`	Search markets by text query
`get_market_history`	Price and volume history (48h, 7d, 30d)
`get_resolution`	Resolution timing and mechanism
`get_execution_guidance`	Optimal trade execution analysis

Screening and discovery

Tool	Description
`screen_markets`	Batch score markets with volume and movement filters
`get_calibration`	Signal accuracy metrics (free, no auth)

Deep research

Tool	Description
`analyze_market`	Trigger a deep AI analysis pipeline (async)
`check_analysis_status`	Poll analysis progress
`get_analysis`	Retrieve completed analysis
`list_analyses`	Browse recent analyses

Strategy and portfolio

Tool	Description
`get_strategy`	Trading signal with Kelly sizing (blocks 30-90s)
`get_portfolio_strategy`	Portfolio-aware signal with correlation
`get_consensus`	Aggregated agent consensus probability

Arbitrage

Tool	Description
`get_arbitrage`	Cached cross-platform opportunities
`get_arbitrage_live`	Fresh scan (10-30 seconds)

Correlation

Tool	Description
`get_correlation`	Cross-market correlation analysis

Trading

Tool	Description
`place_shadow_trade`	Paper trade for tracking
`report_trade`	Report a trade for consensus aggregation
`get_portfolio`	Current positions and P&L
`get_performance`	Win rate, ROI, and edge metrics
`check_resolutions`	Settle resolved markets

Data refresh

Tool	Description
`run_scraper`	Refresh market data from a platform

Webhooks

Tool	Description
`create_webhook`	Register event webhook
`list_webhooks`	List your webhooks
`delete_webhook`	Remove a webhook

MCP vs REST API

Scenario	Use MCP	Use REST API
Interactive research in your IDE	Yes
Automated trading bot		Yes
Quick market check during coding	Yes
Production pipeline		Yes
Portfolio review	Yes
Webhook/streaming integration		Yes
One-off analysis during development	Yes
Scheduled batch processing		Yes

MCP is ideal for exploratory research and ad-hoc queries. The REST API is better for automated systems that run without human interaction.

Tips for effective MCP usage

Be specific about platforms. “Show me Kalshi markets” is faster than “show me markets” because the tool skips Polymarket data. Request expansions explicitly. “Analyze this market and show me the causal factors” triggers the ?expand=causal parameter automatically. Chain analyses. “Analyze the top 3 markets from that screening” works — the assistant remembers prior results and makes sequential tool calls. Use shadow trading. “Place a shadow trade on the Fed market, $500 on YES” lets you track performance without risking real money.

What’s next

MCP tools reference

Full parameter documentation for all 25+ tools.

Build a trading bot

When you are ready to automate, use the REST API.

REST API quickstart

Get an API key and make your first call in 5 minutes.

Getting Started

Core Concepts

Tutorials

Account

MCP Trading Workflow

What this page covers

What is MCP?

Installation

Requirements

Claude Code

Cursor

Verify installation

Workflow examples

1. Market screening

2. Deep analysis

3. Arbitrage hunting

4. Portfolio management

5. Trading signals

6. Market data refresh

Available tools

Market intelligence

Screening and discovery

Deep research

Strategy and portfolio

Arbitrage

Correlation

Trading

Data refresh

Webhooks

MCP vs REST API

Tips for effective MCP usage

What’s next

MCP tools reference

Build a trading bot

REST API quickstart

​What this page covers

​What is MCP?

​Installation

​Requirements

​Claude Code

​Cursor

​Verify installation

​Workflow examples

​1. Market screening

​2. Deep analysis

​3. Arbitrage hunting

​4. Portfolio management

​5. Trading signals

​6. Market data refresh

​Available tools

​Market intelligence

​Screening and discovery

​Deep research

​Strategy and portfolio

​Arbitrage

​Correlation

​Trading

​Data refresh

​Webhooks

​MCP vs REST API

​Tips for effective MCP usage

​What’s next

MCP tools reference

Build a trading bot

REST API quickstart

What this page covers

What is MCP?

Installation

Requirements

Claude Code

Cursor

Verify installation

Workflow examples

1. Market screening

2. Deep analysis

3. Arbitrage hunting

4. Portfolio management

5. Trading signals

6. Market data refresh

Available tools

Market intelligence

Screening and discovery

Deep research

Strategy and portfolio

Arbitrage

Correlation

Trading

Data refresh

Webhooks

MCP vs REST API

Tips for effective MCP usage

What’s next