AgentLadle MCP HKEXnews

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A MCP (Model Context Protocol) server that provides tools for discovering, downloading, parsing, and searching Hong Kong listed company announcements from HKEXnews.

It enables AI assistants (Claude, Cursor, etc.) to access HKEXnews announcement data through 6 structured tools — from discovering available announcements to keyword-searching within their pages.

Scope (v0.1): Announcements and disclosures except full periodic report PDFs (Annual / Interim / Quarterly Report and ESG Report under t1=40000). Performance announcements (Final / Interim / Quarterly Results) are included.

Features

• 6 MCP tools for HKEXnews announcement data: state-driven retrieval (search directly, fallback to download/parse only when needed)
• PDF document parsing using PyMuPDF — physical page extraction into page-split JSON
• Local keyword search with TF + position-boost scoring, zero external search dependencies
• Idempotent — already-downloaded/parsed files are automatically skipped
• Zero-config install — one line to add to your MCP client, no clone or manual setup needed
• Pure Python, cross-platform (Windows / macOS / Linux)

Prerequisites

• Python 3.10+ — Download Python
• uv — Install uv

Note: After installing uv, restart your terminal and MCP client (e.g. Cherry Studio) to ensure the uv command is recognized.

Quick Start

Add to your MCP client configuration (Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "mcp-hkexnews": {
      "command": "uvx",
      "args": ["agentladle-mcp-hkexnews"]
    }
  }
}

That's it. uvx will automatically download the package and its dependencies from PyPI — no clone, no manual install, no path configuration.

Alternative: pip install

If you prefer managing the environment yourself:

pip install agentladle-mcp-hkexnews

Then configure:

{
  "mcpServers": {
    "mcp-hkexnews": {
      "command": "agentladle-mcp-hkexnews"
    }
  }
}

Alternative: Run from source (local development)

Clone the repository and run directly:

git clone https://github.com/agentladle/mcp-hkexnews.git

Then configure your MCP client:

{
  "mcpServers": {
    "mcp-hkexnews": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/mcp-hkexnews", "agentladle-mcp-hkexnews"]
    }
  }
}

Replace /path/to/mcp-hkexnews with the actual path to the cloned repository.

Data Flow

HKEXnews API                      Local Files (~/.agentladle/mcp-hkexnews/data/)
──────────────                    ──────────────────────────────
activestock_sehk_e.json  ──→     companies.json               (stock_code→stockId mapping)
ListOfSecurities.xlsx      ──→         │
tierone/tiertwo JSON       ──→     tiers.json                   (headline category mapping)
                                     │
titleSearchServlet.do    ──→        pdf/{LOCAL_KEY}/            (Tool 2: primary PDF/HTML + manifest)
                                     │
PyMuPDF parsing          ──→        json/*.json                 (Tool 3: parse, page-split)
                                     │
Local TF search          ──→        search results              (Tool 4: keyword search)
Page range read          ──→        page content                (Tool 5: read pages)

Tools

#	Tool	Description
1	list_hkexnews_announcements	Discover available HKEXnews announcements for a company
2	download_hkexnews_announcement	Download announcement PDF (HTML fallback); idempotent
3	parse_hkexnews_announcement	Parse PDF/HTML into page-split JSON using PyMuPDF
4	keyword_search	Full-text keyword search with TF relevance scoring
5	get_announcement_pages	Read announcement content by page number range
6	lookup_stock_code	Diagnostic: look up stock_code→stockId mapping when resolution fails

Tool 1: list_hkexnews_announcements

List available HKEXnews announcements for a company. Use this tool ONLY when the exact date/title is unspecified by the user, or when a download attempt fails due to an ambiguous match. Excludes full periodic report PDFs (Annual / Interim / Quarterly Report and ESG Report under t1=40000).

Parameter	Type	Required	Description
stock_code	string	✅	5-digit HK stock code, e.g. "00700"
category	string	❌	HKEX t1/t2 code or tier name, e.g. "Inside Information", "13500", "20000". Omit to list all in-scope categories
start_date	string	❌	Start date YYYY-MM-DD
end_date	string	❌	End date YYYY-MM-DD
title_keyword	string	❌	Title keyword filter
limit	int	❌	Max announcements to return, default 10, max 50

Tool 2: download_hkexnews_announcement

Download a specific HKEXnews announcement from www1.hkexnews.hk. Prefer local_key from list_hkexnews_announcements when available. Idempotent.

Parameter	Type	Required	Description
stock_code	string	✅	5-digit HK stock code, e.g. "00700"
release_date	string	❌	Release date YYYY-MM-DD (optional if local_key provided)
title_keyword	string	❌	Title substring to disambiguate same-day announcements
category	string	❌	Optional category filter
news_id	string	❌	HKEXnews NEWS_ID if known
local_key	string	❌	Exact local bundle key from list results

Tool 3: parse_hkexnews_announcement

Parse a downloaded announcement PDF/HTML into page-split JSON. Uses PyMuPDF for PDF physical-page text extraction.

Parameter	Type	Required	Description
local_key	string	✅	Bundle key returned by list/download, e.g. "00700_13500_2026-03-15_a1b2c3d4"

Tool 4: keyword_search

Full-text keyword search across all pages. Results ranked by TF + position-boost score.

Parameter	Type	Required	Description
local_key	string	✅	Bundle key
keywords	string[]	✅	1–5 search keywords
match_mode	string	❌	"ANY" (default, any keyword matches) / "ALL" (all must match)
max_results	int	❌	Max results to return, default 5, max 50

Tool 5: get_announcement_pages

Read full page content by page number range.

Parameter	Type	Required	Description
local_key	string	✅	Bundle key
start_page	int	✅	Start page number (1-based)
page_count	int	❌	Number of pages to return, default 3, max 5

Tool 6: lookup_stock_code

Diagnostic tool: look up stock_code→stockId mapping. Use only when download_hkexnews_announcement / list_hkexnews_announcements returns Stock code not found. Bypasses the session failed-code cache.

Parameter	Type	Required	Description
stock_code	string	✅	5-digit HK stock code, e.g. "00700"
refresh	bool	❌	Force re-download of HKEX company mappings (default: false)

Configuration

On first run, a default config file is created at ~/.agentladle/mcp-hkexnews/config.yaml:

paths:
  data_dir: "~/.agentladle/mcp-hkexnews/data"
  pdf_dir: "~/.agentladle/mcp-hkexnews/data/pdf"
  json_dir: "~/.agentladle/mcp-hkexnews/data/json"

download:
  delay_between_requests: 0.3
  min_file_size: 500
  list_row_range: 100
  list_max_pages: 5

company:
  cache_ttl_days: 7

tiers:
  cache_ttl_days: 7

Data Directory Structure

~/.agentladle/mcp-hkexnews/
├── config.yaml                        # Configuration (auto-created)
└── data/
    ├── companies.json                 # stock_code→stockId mapping (auto-downloaded & cached)
    ├── tiers.json                     # HKEX headline category mapping (auto-downloaded & cached)
    ├── pdf/                           # Downloaded announcement bundles
    │   ├── 00700_13500_2026-03-15_a1b2c3d4/
    │   │   ├── primary.pdf
    │   │   └── manifest.json
    │   └── ...
    └── json/                          # Parsed page-split JSON
        ├── 00700_13500_2026-03-15_a1b2c3d4.json
        └── ...

File naming convention: {STOCK_CODE}_{T2_CODE}_{RELEASE_DATE}_{ID_HASH}

Example Usage

The tools are designed with an EAFP (Easier to Ask for Forgiveness than Permission) approach. AI assistants should attempt to retrieve data directly and rely on errors to trigger downloads.

Scenario A: File already exists locally (Shortest Path)

User: "Search 00700 inside information for buyback"

1. keyword_search(local_key="00700_50100_2026-07-09_a1b2c3d4", keywords=["buyback", "repurchase"])
   → Returns page snippets matching the keywords immediately.

Scenario B: File missing (Fallback triggered)

User: "What did Tencent announce in its latest inside information?"

1. list_hkexnews_announcements(stock_code="00700", category="Inside Information", limit=3)
   → Returns local_key / release_date / title.

2. keyword_search(local_key="...", keywords=["inside information"])
   → Error: File not found.

3. download_hkexnews_announcement(stock_code="00700", local_key="...")
   → Downloads PDF to ~/.agentladle/mcp-hkexnews/data/pdf/

4. parse_hkexnews_announcement(local_key="...")
   → Parses into JSON.

5. keyword_search(local_key="...", keywords=["inside information"])
   → Retries search and returns data.

Tech Stack

Component	Choice	Purpose
MCP Framework	mcp (FastMCP)	MCP server with stdio transport
HTTP Client	httpx	HKEXnews API requests & file downloads
PDF Parsing	pymupdf + beautifulsoup4	PDF page text extraction; HTML fallback
Search	Python built-in	TF + position-boost scoring
Config	pyyaml	YAML configuration file
Securities List	openpyxl	Parse HKEX ListOfSecurities.xlsx

Project Structure

src/mcp_hkexnews/
├── __init__.py
├── server.py                 # MCP Server entry point
├── config.py                 # Config loading (~/.agentladle/mcp-hkexnews/config.yaml, singleton cached)
├── models.py                 # Data models
├── categories.py             # Announcement category blacklist
├── response.py               # Unified JSON responses
├── instances.py              # Service singletons
├── tools/
│   ├── list_announcements.py # Tool 1: list_hkexnews_announcements
│   ├── download.py           # Tool 2: download_hkexnews_announcement
│   ├── parse.py              # Tool 3: parse_hkexnews_announcement
│   ├── search.py             # Tool 4: keyword_search
│   ├── page.py               # Tool 5: get_announcement_pages
│   └── lookup.py             # Tool 6: lookup_stock_code
└── services/
    ├── company.py            # HKEX activestock + ListOfSecurities + stock_code→stockId
    ├── tiers.py              # HKEX tierone/tiertwo category cache
    ├── downloader.py         # HKEXnews titleSearch + PDF download
    ├── parser.py             # PDF/HTML→JSON parsing (PyMuPDF)
    ├── searcher.py           # Local JSON search + TF scoring
    └── keys.py               # local_key helpers

License

MIT