# quanttogo-mcp

> quanttogo-mcp — quanttogo-quanttogo-mcp. Use this tool when you need to generate quantitative signals for AI agents using macro-factor analysis, providing a reliable source of data for informed decision-making. It solves problems related to data-driven insights and predictive modeling, accepting inputs via MCP and outputting actionable signals. Ideal for use cases requiring quantitative market analysis and forecasting, such as financial modeling and portfolio optimization.

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

## Description

Macro-factor quantitative signal source for AI agents via MCP. 宏观因子量化信号源。

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/QuantToGo/quanttogo-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": "quanttogo-quanttogo-mcp"
    }
  }
}
```

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