QuantToGo/quanttogo-mcp

Overview

QuantToGo/quanttogo-mcp is a Python-based MCP server that provides macro-factor quantitative signals to AI agents. It exposes structured financial data through the Model Context Protocol, enabling agents to consume factor-based market signals.

Best for

Best for
Developers building AI agents that need structured macro-factor financial signals

Use cases

• Feed macro-factor signals into an AI trading or analysis agent
• Integrate quantitative market data into agent workflows via MCP
• Build automated research pipelines that consume factor-based indicators

How to use

Tools exposed

• list_strategies
• get_strategy_performance
• compare_strategies
• get_index_data
• get_subscription_info
• register_trial
• get_signals
• check_subscription

Tested with

Claude Desktop, Claude Code, Cursor, Coze, Remote SSE, Remote Streamable HTTP

Example client config

{\n  "mcpServers": {\n    "quanttogo": {\n      "command": "npx",\n      "args": ["-y", "quanttogo-mcp"]\n    }\n  }\n}

Notes

QuantToGo/quanttogo-mcp is a Python-based MCP server that provides macro-factor quantitative signals to AI agents. It exposes structured financial data through the Model Context Protocol, enabling agents to consume factor-based market signals.

6 stars on GitHub. Last updated 2026-06-01. Licensed MIT.

Use cases

• Feed macro-factor signals into an AI trading or analysis agent
• Integrate quantitative market data into agent workflows via MCP
• Build automated research pipelines that consume factor-based indicators

Pros

• Leverages the MCP standard for agent-tool interoperability
• Python codebase is straightforward to extend or customize
• Focused on macro-factor signals, a niche but valuable data type

Cons

• Very early stage with only 6 GitHub stars and limited community
• No documentation or usage examples beyond the repository name
• Dependency on external macro-factor data sources not specified

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.