ApocData MCP server
2026년 10월 14일 커밋AI 에이전트와 리서치를 위해 공시 분석, 재무 기본 지표, 자금 흐름을 포함한 구조화된 A주 시장 데이터를 제공합니다.
다른 이름 ApocData/ApocData-skill
- 스타
- 104
- Tools
- —
- 다운로드, 30일
- —
- 최신 버전
- —
- 마지막 커밋
- 2026년 10월 14일
- 라이선스
- Apache-2.0
ApocData MCP란?
ApocData은(는) MCP server입니다. AI 에이전트와 리서치를 위해 공시 분석, 재무 기본 지표, 자금 흐름을 포함한 구조화된 A주 시장 데이터를 제공합니다.
프로젝트 README에서 생성된 요약
ApocData MCP server 정보
ApocData-skill은 A주 시장에 집중하는 AI 에이전트 및 퀀트 리서치를 위해 특별히 설계된 구조화된 시장 데이터를 제공합니다. 가공되지 않은 PDF 공시를 AI 생성 요약, 감성 분석, 중요도 수준을 포함한 기계 판독 가능 형식으로 변환합니다. 이를 통해 대규모 언어 모델이 수동 파싱 없이 금융 이벤트에 대해 추론할 수 있습니다.
이 MCP server는 시세, 재무, 자금 흐름, 매크로 지표 및 용호보(dragon-tiger lists)를 다루는 46개의 도구와 45개 이상의 엔드포인트를 제공합니다. Claude Desktop, Cursor, ChatGPT를 비롯하여 DeepSeek 또는 Kimi와 같은 에이전트 등 다양한 AI 클라이언트와 호환됩니다. 사용자는 MCP 호환 환경을 위해 npx를 통해 설정하거나 직접 HTTP 엔드포인트를 사용할 수 있습니다.
등록 없이 즉시 접속 가능한 무료 티어가 제공되지만, 더 높은 할당량과 심층적인 과거 데이터를 사용하려면 등록할 수 있습니다. 이 서비스는 전환사채, 섹터, 상한가 리뷰와 같은 특정 데이터 그룹을 포함하여 중국 주식 시장에 대한 포괄적인 관점을 제공합니다.
활용 사례
- 1비교 가치 평가 분석 수행개발자가 에이전트에게 특정 종목의 가치 평가 지표를 업계 동종 기업과 비교하도록 요청할 수 있습니다.
- 2고위험 증권 모니터링에이전트가 ST 종목의 최근 주요 공시를 식별하고 요약하여 잠재적 리스크를 평가할 수 있습니다.
- 3자금 이동 트렌드 추적사용자가 어떤 시장 섹터에 북향 자금이 가장 많이 유입되고 있는지 요약을 요청할 수 있습니다.
프로젝트의 README를 바탕으로 작성되었습니다. 모든 활용 사례는 해당 내용의 인용문과 대조하여 확인합니다.
자동 검사
2026년 11월 14일에 실행됨전체 보고서 →아직 인증되지 않았습니다. 서버는 80점 이상의 점수, 심각한 결함 없음, 그리고 검사에 충분한 데이터를 갖추어야 합니다. 검사 방식 · 점수를 높이기 위해 수정할 항목
- 소스 저장소 연결 및 접근 가능apocdata/apocdata-skill is public and reachablegithub.com/apocdata/apocdata-skill ↗통과
- 패키지가 이 저장소를 소스로 명시검사되지 않음
- 빌드 출처(Sigstore 증명)검사되지 않음
- 오픈 소스 라이선스 (OSI)Apache-2.0spdx.org/licenses/Apache-2.0.html ↗통과
- 최근 1년 내 커밋 또는 릴리스Last commit or release 2026-10-14github.com/apocdata/apocdata-skill ↗통과
- OpenSSF Scorecard검사되지 않음
- 패키지 이름이 인기 패키지와 유사하지 않음검사되지 않음
- 패키지 및 해당 종속성에 중요한 보안 권고 없음검사되지 않음
- 모델에 대한 숨겨진 지침 없음0 tool descriptions and the README read; no override phrases, credential paths or hidden characters통과
- 파괴적인 도구에 라벨 표시됨검사되지 않음
- 원격 엔드포인트가 MCP 핸드셰이크에 응답검사되지 않음
README
GitHub에서 보기天启至数 ApocData — A股数据服务 (ApocData A-Share Data Skill)
Let AI read A-share announcements
The A-share announcements and fundamentals database built for AI · parsed · structured · traceable
All-market announcements parsed into structured data: AI summary · category · importance · sentiment, fully traceable to source. Also 45+ endpoints for quotes, financials, capital flows, dragon-tiger lists and macro data. Free tier for instant access; register for higher quotas. Compatible with WorkBuddy, Codex, Hermes, DeepSeek, Kimi, Qwen, Zhipu and any AI agent that supports tool calling.
The SKILL.md in this repository is a capability card that can
be loaded directly into AI agents, letting LLMs query A-share market data
and complete research tasks without any SDK.
Quick Try
Copy-paste and run, no config needed:
curl -s "https://www.apocdata.com/api/blade-dataplatform/open/data/quote?symbol=600519"
{
"code": 200, "success": true,
"data": {
"symbol": "600519", "name": "Kweichow Moutai",
"close": 1528.00, "pct_chg": -0.52,
"volume": 2856321, "delayed_minutes": 15
}
}
Actual response has more fields; above is a simplified example.
Announcement Parsing — from PDF to structured data
Conventional data sources hand you the raw announcement text and leave the reading and parsing to you. ApocData returns the parsed, structured result, ready for an AI agent to consume.
Every announcement carries:
- AI summary (
summary) — the key facts extracted, no need to read the full document - Category (
category) — earnings, dividend, buyback, share increase/decrease, litigation and more, filterable programmatically - Importance level (
importance) — locate the announcements that actually matter - Sentiment (
sentiment) — positive / negative / neutral, to help judge the direction of impact - Source link (
url) — every conclusion traces back to the original announcement
Also returned: title, ann_date, publish_time, keywords, source, and
content (full Markdown body, on includeContent=true).
Covers all-market announcements, including Stock Connect constituents, ST and delisting-risk securities.
Why ApocData?
| ApocData | 数据源A | 数据源B | 数据源C | |
|---|---|---|---|---|
| Announcement parsing | Structured + AI summary + sentiment | Raw text | Raw text | Raw text |
| Access | Free tier, no registration | Paid token | Python env + install | Application approval |
| Setup steps | 0 | 3+ (register → token → config) | 2+ (pip + deps) | Manual review |
| AI Agent native | Skill + MCP | Plugin (legacy) | Not supported | Not supported |
| curl one-liner | Direct | Need SDK | Need SDK | Need SDK |
| A-share endpoints | 45 | 100+ (paywalled) | 100+ (free) | 200+ (paid) |
| MCP support | 46 tools | None | None | None |
Comparison based on publicly available info as of 2026-08.
Core Capabilities
- Announcement parsing — all-market announcements parsed by AI into structured fields
(
title/category/importance/ann_date/summary/sentiment/url), directly usable for reasoning and traceable to the source document (mainstream data sources mostly ship announcements as raw text) - Full-dimension coverage — quotes, financials, valuation, capital flows, limit-up review, dragon-tiger lists, sectors & concepts, convertible bonds, macro indicators and trading calendar: 11 groups, 45+ endpoints, one consistent definition set
- Multiple integration paths — Skill package (one-command install), MCP server (46 tools), plain HTTP endpoints (direct curl), covering every mainstream AI ecosystem
- Traceable — every data point traces back to the original announcement, so AI conclusions can be checked
Table of Contents
- Announcement Parsing
- Why ApocData?
- Core Capabilities
- Overview
- Use Cases
- Data Service Platform
- Installation
- Project Structure
- Basic Usage
- Endpoint Overview
- Editions & Capabilities
- Notes & Compliance
Overview
ApocData is the A-share announcements and data layer built for AI: parsed, structured, traceable. Announcement parsing is the core — every announcement becomes structured data an agent can reason over directly — and around it sits full-dimension coverage of quotes, financials, capital flows, dragon-tiger lists, quantitative factors and macro indicators, designed for AI agents, quantitative research, investment content, and institutional applications.
Use Cases
- "What are the key points in Kweichow Moutai's recent earnings flash?" → key figures extracted as structured fields, traceable to the original announcement
- "Analyse the valuation of 688017 — how does it compare with its peers?" → PE/PB/market cap aggregated with financial metrics and the last 30 sessions
- "Which ST stocks published material announcements in the past 30 days?" → ranked by risk level, each with an AI summary and a source link
- "Which sectors has northbound capital been flowing into?" → Stock Connect plus sector capital flows, with the top inflow sectors summarised automatically
Data Service Platform
Platform URL: https://www.apocdata.com
The data service platform is the unified entry point for ApocData. It provides:
- Open API (free tier) — This is what the Skill connects to. A free trial quota is callable straight away, ideal for quick validation and lightweight usage.
- OpenAPI documentation & SDKs — Python / TypeScript supported.
- Registered & Professional editions (with API key) — Higher quotas, deeper history, lower latency, and richer data fields. See Editions & Capabilities.
The base URL used by this Skill:
https://www.apocdata.com/api/blade-dataplatform/open/data
Installation
Quick Install (recommended)
mkdir -p ~/.claude/skills/apocdata
curl -sL https://gitee.com/apocdata/ApocData-skill/repository/archive/v2.0.5.tar.gz \
| tar xz -C ~/.claude/skills/apocdata --strip-components=1
Restart Claude Code and the skill will be auto-detected.
MCP Install (Claude Desktop / Cursor / ChatGPT)
{
"mcpServers": {
"apocdata": {
"command": "npx",
"args": ["-y", "@apocdata-info/mcp-server"]
}
}
}
OpenAPI Import (GPT Actions / Dify / Coze / n8n)
https://www.apocdata.com/api/blade-dataplatform/open/data/openapi.json
Import and use — free tier, no registration needed.
Alternative: install script
curl -sL https://gitee.com/apocdata/ApocData-skill/raw/main/scripts/install.sh | bash
Project Structure
This Skill uses a multi-file structure for progressive loading — the entry
SKILL.md is a slim router (~180 lines), and detailed endpoint specs are
loaded on demand from the references/ directory:
├── SKILL.md # Entry point (slim router, ~180 lines)
├── README.md # This file (human-facing docs)
├── CHANGELOG.md # Version history
├── references/ # On-demand reference docs (loaded per topic)
│ ├── boundaries.md # Interface boundaries, headers, error codes, cache, freshness
│ ├── group-a-quote.md # A. Quotes & Valuation (10 endpoints)
│ ├── group-b-financial.md # B. Financials & Fundamentals (8 endpoints)
│ ├── group-c-capital.md # C. Capital Flow (7 endpoints)
│ ├── group-d-limitup.md # D. Limit-up & Sentiment (4 endpoints)
│ ├── group-e-events.md # E. Events & Information (3 endpoints)
│ ├── group-f-sector.md # F. Sectors & Concepts (4 endpoints)
│ ├── group-g-convertible.md # G. Convertible Bonds (2 endpoints)
│ ├── group-h-quant.md # H. Quant & Technical (2 endpoints)
│ ├── group-i-macro.md # I. Macro (3 endpoints)
│ ├── group-j-tools.md # J. Tools (1 endpoint)
│ ├── group-k-agent.md # K. Agent Enhanced (2 endpoints)
│ ├── examples.md # Multi-endpoint analysis examples (5 scenarios)
│ └── safety-rules.md # Financial output safety constraints (6 rules)
└── scripts/
└── install.sh # One-line install script
Why multi-file? A simple query like "what's the price of Moutai" only needs the entry SKILL.md + one reference file (~4K tokens), instead of loading the entire 47KB monolith (~15K tokens). This reduces token consumption by 70%+.
Basic Usage
All endpoints are HTTP GET, called with curl:
BASE="https://www.apocdata.com/api/blade-dataplatform/open/data"
# Get real-time quote
curl -s "$BASE/quote?symbol=000001"
# Get stock info (PE/PB/market cap)
curl -s "$BASE/stock?symbol=000001"
# Get comprehensive profile (8 dimensions in one call)
curl -s "$BASE/profile/full?symbol=688017"
Endpoint Overview
| Group | Topic | Endpoints |
|---|---|---|
| A | Quotes & Valuation | 10 |
| B | Financials & Fundamentals | 8 |
| C | Capital Flow | 7 |
| D | Limit-up & Sentiment | 4 |
| E | Events & Information | 2 (+ 1 deprecated) |
| F | Sectors & Concepts | 4 |
| G | Convertible Bonds | 2 |
| H | Quant & Technical | 2 |
| I | Macro | 3 |
| J | Tools | 1 |
| K | Agent Enhanced | 2 |
| Total | 45 active (+ 1 deprecated) |
Editions & Capabilities
ApocData is organized into four editions to match different use cases. All editions share the same API contract; capabilities are filtered by edition whitelist. This Skill defaults to the free-tier open endpoints. Register to unlock higher quotas and more data capabilities.
Edition Lineup
| SKU | Edition | Target Users |
|---|---|---|
| FREE | Free | Individuals / AI agent demos |
| PRO | Professional | Investment research / content creators |
| QUANT | Quant | Quantitative research teams |
| ENT | Enterprise | Institutional embedding / private deployment |
Capability Comparison (selected)
| Capability | Free | Pro | Quant | Enterprise |
|---|---|---|---|---|
| Daily calls | 2,000 | 50,000 | 300,000 | Custom |
| QPS limit | 2 | 10 | 30 | Custom |
| Snapshot latency | 15 min | 1 min | ≤ 30s | Custom (≤ 10s) |
| Daily K depth | 30 days | 5 yrs + adj. | Full | Full |
| Quant factors | — | 20 | Full | Full + custom |
| MCP Tools | 8 | 14 | 18 | 18+ custom |
| Availability SLO | best-effort | 99.0% | 99.5% | 99.9% (contract) |
| Support | Community | Email 48h | Email 24h | Dedicated channel + phone |
Endpoint Verification
Last verified: 2026-08-12 12:21 CST
| Check | Result |
|---|---|
| Core endpoints tested | 12/12 -> HTTP 200 |
| Total active endpoints | 45 (across 11 groups) |
| Avg latency | ~217ms |
| Deprecated | /news (HTTP 410, documented in group-e) |
Verified endpoints: quote, stock, daily, financial, moneyflow, hsgt,
limit-list, announcements, concepts, macro/latest, calendar, profile/full.
Full 45-endpoint coverage is validated via the scenario quick-reference table in SKILL.md.
Notes & Compliance
- Free tier: callable without registration, suited to quick validation and light usage
- Registered users: higher quotas and more data capabilities (see the platform editions page)
- All endpoints are read-only
- Data source: ApocData Cloud (天启云), synced with A-share market data
- Free tier quotes have a ~15-minute delay (see
delayed_minutesin response) - Announcements: T+0 at 08:00; Northbound capital: 20:00
- This Skill is for research assistance only — not investment advice
- Financial output safety constraints are enforced via
references/safety-rules.md
Get Started
Free tier, callable without registration; registered users unlock higher quotas and more data capabilities.
Try free → · Register → · Star on Gitee →
License
See LICENSE for details.
ApocData MCP 질문
공시 분석은 어떻게 이루어지나요?
ApocData가 가공되지 않은 공시 데이터를 AI 요약, 카테고리, 중요도, 감성 분석과 같은 구조화된 필드로 파싱하여 AI가 즉시 추론할 수 있도록 만듭니다.
무료 버전을 사용할 수 있나요?
네, 즉시 접속 및 가벼운 용도로 사용할 수 있는 무료 티어가 있습니다. 다만 더 높은 할당량을 사용하려면 등록이 필요합니다.
Claude 또는 ChatGPT와 함께 사용할 수 있나요?
네, Claude, ChatGPT, Qwen, Kimi, DeepSeek을 포함하여 도구 호출(tool calling)을 지원하는 모든 AI 에이전트와 호환됩니다.