WalnutLink A-share Depth Desk

China A-share filings: why a number moved, does the trend continue, who supplies whom.

사용해야 할까요

품질 및 안전성

A
설명 품질
100%
스키마 완전성
100%
이름 품질
80%
오염 위험
100%
권한 일치
100%
프로토콜 준수
100%

도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.

컨텍스트 비용

~641토큰 (도구 정의)
~840 B일반적인 응답 크기
중간 정도의 주의 영향 (128k 컨텍스트의 0.50%)

이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.

설치

원클릭 설치

`claude_desktop_config.json` 파일에 다음을 추가하세요:

{
  "mcpServers": {
    "walnutlink-a-share": {
      "url": "https://walnutlink.ai/mcp"
    }
  }
}

원격 엔드포인트

https://walnutlink.ai/mcpstreamable-http

할 수 있는 일

도구 목록

도구 (3)

🟢 읽기 전용🟡 쓰기🔴 삭제⚪ 알 수 없음
⚪a_share_deep_research(question)

Use when the user asks WHY something happened or WHAT it means for a Chinese listed company: a margin moved, a policy landed, an industry chain shifted, two companies diverge, or what management actually said on the record. This desk does NOT serve quotes, price checks or valuation snapshots - that is commodity data available everywhere, and answering it would waste your turn. What it does: reads the filed report, the IR transcript and the disclosure wording, then answers in numbers. Returns a vetted answer immediately if this question has been researched before; otherwise it returns the primary-source evidence it already holds plus an id for the human answer. Ask it the question you would ask a sell-side analyst, not the one you would type into a quote app.

입력 스키마

{
  "type": "object",
  "properties": {
    "question": {
      "type": "string",
      "description": "The judgement question, in Chinese or English. Name the company; a ticker alone is usually a quote request."
    }
  },
  "required": [
    "question"
  ]
}
⚪a_share_chain_edge(a, b, relation)

Use when the user asks HOW two Chinese companies relate: who supplies whom, who competes with whom, who owns equity in whom, or whether an acquisition target overlaps with the acquirer's product line. Every relationship returned carries a verbatim quote from a filed document (annual report, prospectus, restructuring draft, related-party announcement) and a confidence tag: primary = the filing says it outright, secondary = broker or media, inferred = our deduction with no direct textual proof. Unlisted companies work as inputs too, which matters for acquisition targets. If the pair has not been researched it says so plainly instead of guessing - absence of a record is not evidence of no relationship. Do NOT call this for prices, valuation or index levels.

입력 스키마

{
  "type": "object",
  "properties": {
    "a": {
      "type": "string",
      "description": "Company A: 6-digit A-share code or Chinese name."
    },
    "b": {
      "type": "string",
      "description": "Company B: 6-digit code or name. An unlisted company name also works (e.g. an acquisition target)."
    },
    "relation": {
      "type": "string",
      "description": "Optional filter when the pair has more than one link.",
      "enum": [
        "supplier",
        "customer",
        "competitor",
        "peer",
        "equity",
        "investor",
        "none",
        "unknown"
      ]
    }
  },
  "required": [
    "a",
    "b"
  ]
}
🟢a_share_answer_by_id(id)

Retrieve a research result by the id that a_share_deep_research returned. Call this to check whether the human-reviewed answer is ready, or to fetch the full text once it is. Answers take minutes to hours because a filed report is read first; while it is still queued the tool returns the queue position, not a guess. Use this instead of opening the public page when you are an automated client that cannot render HTML.

입력 스키마

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "The 12-character hex id returned by a_share_deep_research, e.g. dc037a7fc6c8."
    }
  },
  "required": [
    "id"
  ]
}

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