# causal-chain-mcp

> causal-chain-mcp — jingxuanc-causal-chain-mcp. Use this tool when you need to analyze causal relationships between events and targets, calculate cumulative abnormal returns (CAR), and conduct knowledge graph propagation and LLM conduction analysis. It solves problems related to event-target matching, risk assessment, and decision-making by providing a comprehensive framework for causal chain analysis. The tool takes in event and target data as inputs and outputs matching results, CAR calculations, and propagation analysis, making it suitable for applications in finance and risk management.

Canonical page: https://skillsregistry.net/skills/jingxuanc-causal-chain-mcp  
JSON: https://api.skillsregistry.net/v1/skills/jingxuanc-causal-chain-mcp

## Description

A股因果链推演 MCP：事件↔标的匹配 / CAR 计算 / 知识图谱传播 / LLM 传导分析 / 反驳评级，13 工具，与 news-mcp 联动

## Trust

- **Trust score (0–1):** 0.86
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/JingxuanC/causal-chain-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": "jingxuanc-causal-chain-mcp"
    }
  }
}
```

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