How do I use AgentLadle MCP CNINFO?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AgentLadle MCP CNINFO List recent announcements for stock code 000001" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

AgentLadle MCP CNINFO

by agentladle

Overview Schema Related Servers Score Discussions

Python

Local

AgentLadle MCP CNINFO

English | 中文

🇨🇳 China A-Share Annual Reports — Cloud-hosted MCP for Shanghai & Shenzhen listed companies. Read more | Get API Key

A MCP (Model Context Protocol) server that provides tools for discovering, downloading, parsing, and searching China A-share announcements from CNINFO (巨潮资讯网).

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

Scope (v0.1): Announcements only. Periodic reports (年报 / 半年报 / 一季报 / 三季报) are out of scope.

Features

6 MCP tools for CNINFO 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)

Related MCP server: Chinese Stock MCP

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

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-cninfo

Then configure:

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

Alternative: Run from source (local development)

Clone the repository and run directly:

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

Then configure your MCP client:

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

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

Data Flow

CNINFO API                        Local Files (~/.agentladle/mcp-cninfo/data/)
──────────────                    ──────────────────────────────
szse_stock.json        ──→       companies.json               (stock_code→orgId mapping)
                                     │
hisAnnouncement/query  ──→        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_cninfo_announcements`	Discover available CNINFO announcements for a company
2	`download_cninfo_announcement`	Download announcement PDF (HTML fallback); idempotent
3	`parse_cninfo_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→orgId mapping when resolution fails

Tool 1: `list_cninfo_announcements`

List available CNINFO 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. Default categories exclude periodic reports (年报 / 半年报 / 一季报 / 三季报).

Parameter	Type	Required	Description
`stock_code`	string	✅	6-digit stock code, e.g. `"000001"`
`category`	string	❌	Category key, short code, or Chinese label, e.g. `"董事会"`, `"DSH"`, `"category_dshgg_szsh"`. Omit to list default announcement 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_cninfo_announcement`

Download a specific CNINFO announcement from static.cninfo.com.cn. Prefer local_key from list_cninfo_announcements when available. Idempotent.

Parameter	Type	Required	Description
`stock_code`	string	✅	6-digit stock code, e.g. `"000001"`
`announce_date`	string	❌	Announce date `YYYY-MM-DD` (optional if `local_key` provided)
`title_keyword`	string	❌	Title substring to disambiguate same-day announcements
`category`	string	❌	Optional category filter
`announcement_id`	string	❌	CNINFO announcement id if known
`local_key`	string	❌	Exact local bundle key from list results

Tool 3: `parse_cninfo_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. `"000001_DSH_2026-07-02_8b1ad607"`

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→orgId mapping. Use only when download_cninfo_announcement / list_cninfo_announcements returns Stock code not found. Bypasses the session failed-code cache.

Parameter	Type	Required	Description
`stock_code`	string	✅	6-digit stock code, e.g. `"000001"`
`refresh`	bool	❌	Force re-download of `szse_stock.json` from CNINFO (default: `false`)

Configuration

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

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

download:
  delay_between_requests: 0.3
  min_file_size: 500
  list_page_size: 30
  list_max_pages: 5

company:
  cache_ttl_days: 7

Data Directory Structure

~/.agentladle/mcp-cninfo/
├── config.yaml                        # Configuration (auto-created)
└── data/
    ├── companies.json                 # stock_code→orgId mapping (auto-downloaded & cached)
    ├── pdf/                           # Downloaded announcement bundles
    │   ├── 000001_DSH_2026-07-02_8b1ad607/
    │   │   ├── primary.pdf
    │   │   └── manifest.json
    │   └── ...
    └── json/                          # Parsed page-split JSON
        ├── 000001_DSH_2026-07-02_8b1ad607.json
        └── ...

File naming convention: {STOCK_CODE}_{CAT_SHORT}_{ANNOUNCE_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 000001 board resolution for 回购"

1. keyword_search(local_key="000001_DSH_2026-07-02_8b1ad607", keywords=["回购", "决议"])
   → Returns page snippets matching the keywords immediately.

Scenario B: File missing (Fallback triggered)

User: "What did Ping An Bank announce in its latest board notice?"

1. list_cninfo_announcements(stock_code="000001", category="董事会", limit=3)
   → Returns local_key / announce_date / title.

2. keyword_search(local_key="...", keywords=["董事会", "决议"])
   → Error: File not found.

3. download_cninfo_announcement(stock_code="000001", local_key="...")
   → Downloads PDF to ~/.agentladle/mcp-cninfo/data/pdf/

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

5. keyword_search(local_key="...", keywords=["董事会", "决议"])
   → Retries search and returns data.

Tech Stack

Component	Choice	Purpose
MCP Framework	`mcp` (FastMCP)	MCP server with stdio transport
HTTP Client	`httpx`	CNINFO 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

Project Structure

src/mcp_cninfo/
├── __init__.py
├── server.py                 # MCP Server entry point
├── config.py                 # Config loading (~/.agentladle/mcp-cninfo/config.yaml, singleton cached)
├── models.py                 # Data models
├── categories.py             # Announcement category whitelist / blacklist
├── response.py               # Unified JSON responses
├── instances.py              # Service singletons
├── tools/
│   ├── list_announcements.py # Tool 1: list_cninfo_announcements
│   ├── download.py           # Tool 2: download_cninfo_announcement
│   ├── parse.py              # Tool 3: parse_cninfo_announcement
│   ├── search.py             # Tool 4: keyword_search
│   ├── page.py               # Tool 5: get_announcement_pages
│   └── lookup.py             # Tool 6: lookup_stock_code
└── services/
    ├── company.py            # CNINFO szse_stock.json + stock_code→orgId
    ├── downloader.py         # CNINFO query + PDF download
    ├── parser.py             # PDF/HTML→JSON parsing (PyMuPDF)
    ├── searcher.py           # Local JSON search + TF scoring
    └── keys.py               # local_key helpers

License

MIT

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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