This server provides MCP tools to discover, download, parse, and search China A-share company announcements from CNINFO (巨潮资讯网), enabling AI assistants to retrieve and analyze announcement content.
List announcements: Discover available announcements for a stock code, optionally filtered by category, date range, or title keyword.
Download announcements: Download the announcement PDF (or HTML fallback) to local storage using stock code, date, title, or a unique local key.
Parse announcements: Convert downloaded PDF/HTML into page-split JSON for structured access.
Keyword search: Perform full-text keyword search across a parsed announcement, ranked by relevance (TF + position-boost).
Read pages: Retrieve full content of specific page ranges from a parsed announcement.
Diagnostic lookup: Resolve stock code to CNINFO orgId mapping when standard calls fail, with optional cache refresh.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
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.
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🇨🇳/🇭🇰 Cloud-hosted MCP for A-share & HK listed companies (Past 3 years annual & latest interim reports). 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.
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
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
uvcommand is recognized.
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.
If you prefer managing the environment yourself:
pip install agentladle-mcp-cninfoThen configure:
{
"mcpServers": {
"mcp-cninfo": {
"command": "agentladle-mcp-cninfo"
}
}
}Clone the repository and run directly:
git clone https://github.com/agentladle/mcp-cninfo.gitThen 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.
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)# | Tool | Description |
1 |
| Discover available CNINFO announcements for a company |
2 |
| Download announcement PDF (HTML fallback); idempotent |
3 |
| Parse PDF/HTML into page-split JSON using PyMuPDF |
4 |
| Full-text keyword search with TF relevance scoring |
5 |
| Read announcement content by page number range |
6 |
| Diagnostic: look up stock_code→orgId mapping when resolution fails |
list_cninfo_announcementsList 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 |
| string | ✅ | 6-digit stock code, e.g. |
| string | ❌ | Category key, short code, or Chinese label, e.g. |
| string | ❌ | Start date |
| string | ❌ | End date |
| string | ❌ | Title keyword filter |
| int | ❌ | Max announcements to return, default 10, max 50 |
download_cninfo_announcementDownload a specific CNINFO announcement from static.cninfo.com.cn. Prefer local_key from list_cninfo_announcements when available. Idempotent.
Parameter | Type | Required | Description |
| string | ✅ | 6-digit stock code, e.g. |
| string | ❌ | Announce date |
| string | ❌ | Title substring to disambiguate same-day announcements |
| string | ❌ | Optional category filter |
| string | ❌ | CNINFO announcement id if known |
| string | ❌ | Exact local bundle key from list results |
parse_cninfo_announcementParse a downloaded announcement PDF/HTML into page-split JSON. Uses PyMuPDF for PDF physical-page text extraction.
Parameter | Type | Required | Description |
| string | ✅ | Bundle key returned by list/download, e.g. |
keyword_searchFull-text keyword search across all pages. Results ranked by TF + position-boost score.
Parameter | Type | Required | Description |
| string | ✅ | Bundle key |
| string[] | ✅ | 1–5 search keywords |
| string | ❌ |
|
| int | ❌ | Max results to return, default 5, max 50 |
get_announcement_pagesRead full page content by page number range.
Parameter | Type | Required | Description |
| string | ✅ | Bundle key |
| int | ✅ | Start page number (1-based) |
| int | ❌ | Number of pages to return, default 3, max 5 |
lookup_stock_codeDiagnostic 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 |
| string | ✅ | 6-digit stock code, e.g. |
| bool | ❌ | Force re-download of |
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~/.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}
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.Component | Choice | Purpose |
MCP Framework |
| MCP server with stdio transport |
HTTP Client |
| CNINFO API requests & file downloads |
PDF Parsing |
| PDF page text extraction; HTML fallback |
Search | Python built-in | TF + position-boost scoring |
Config |
| YAML configuration file |
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 helpersMIT
Download a CNINFO announcement PDF (or HTML fallback) to local storage.
Args: stock_code: 6-digit stock code announce_date: YYYY-MM-DD (optional if local_key provided) title_keyword: Title substring to disambiguate same-day announcements category: Optional category filter announcement_id: CNINFO announcement id if known local_key: Exact local bundle key from list results
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | ||
| local_key | No | ||
| stock_code | Yes | ||
| announce_date | No | ||
| title_keyword | No | ||
| announcement_id | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behaviors: downloads to local storage, may fallback to HTML, and that periodic reports are out of scope. Also directs to call parse_cninfo_announcement after success. Lacks details on file storage location or overwrite behavior, but overall transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with labeled sections (<strategy>, <critical_rules>, Args). Front-loaded with core purpose. Every sentence contributes value. Slightly long but efficient for the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and an output schema present, the description covers usage conditions, parameter details, relationship to siblings, and exclusions. It tells the agent when to invoke and what to do after success, making it fully contextualized.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description compensates by listing each parameter with brief but useful semantics: stock_code is 6-digit, announce_date is YYYY-MM-DD (optional if local_key), title_keyword disambiguates, etc. Adds meaning beyond schema names and types, though not extremely detailed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it downloads a CNINFO announcement PDF (or HTML fallback) to local storage. It specifies the resource (CNINFO announcement) and action (download). Distinguishes from siblings by being a fallback tool and having explicit conditions for invocation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: 'Invoke ONLY as a fallback when keyword_search / get_announcement_pages returns file not found, or after list when you have a concrete local_key / announcement_id.' Also provides critical rules about preferring local_key and that periodic reports are out of scope, giving clear guidance on alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve full page content for a range of pages from a parsed announcement.
Args: local_key: Bundle key start_page: 1-based start page page_count: Number of pages, default 3, max 5
| Name | Required | Description | Default |
|---|---|---|---|
| local_key | Yes | ||
| page_count | No | ||
| start_page | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It adds behavioral context through strategy and critical rules (e.g., default page_count of 3, max 5). However, it does not mention error handling for invalid start_page or missing local_key, nor any authorization or rate limits. Still, the provided rules are helpful and non-contradictory.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by a strategy section and critical rules. Each sentence adds value without redundancy. The Args section is cleanly organized. Length is appropriate for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (3 parameters, output schema present), the description covers purpose, usage, and parameters well. It omits error conditions or return format, but the output schema may provide that. Slightly lacking in describing what happens on failure, but overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must add meaning. It does: local_key is 'bundle key', start_page is '1-based start page', page_count has default and max. This fully compensates for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Retrieve full page content for a range of pages from a parsed announcement.' This is a specific verb-resource pair, and it distinguishes from sibling tools like keyword_search (for specific facts) and download_cninfo_announcement (whole file).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The <strategy> explicitly instructs to invoke after keyword_search provides a start_page, and to not pre-check file existence. The <critical_rules> recommend preferring keyword_search for specific facts. This clearly guides when and how to use the tool, including exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search a parsed announcement by keywords. Results sorted by relevance.
Args: local_key: Bundle key from list/download keywords: 1-5 search keywords match_mode: "ANY" or "ALL", default ANY max_results: Max snippets, default 5, max 50
| Name | Required | Description | Default |
|---|---|---|---|
| keywords | Yes | ||
| local_key | Yes | ||
| match_mode | No | ANY | |
| max_results | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It discloses sorting by relevance, match modes, default values, and max results limit. It also hints at error handling (prompt download/parse on missing file) but lacks details on edge cases like zero results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with strategy and rules sections. Each sentence adds value. The Args list is clear. The use of HTML-like tags adds structure but could be considered nonstandard; however, it aids readability and does not waste space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, usage, parameters, and error handling. Given the existence of an output schema, lack of return format description is acceptable. However, it could mention snippet highlighting or result ordering more explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description fully explains all four parameters: local_key ('Bundle key from list/download'), keywords ('1-5 search keywords'), match_mode ('ANY or ALL, default ANY'), and max_results ('Max snippets, default 5, max 50'). This adds critical constraints and context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search a parsed announcement by keywords' with specific verb and resource. It distinguishes from sibling tools like download, list, parse, etc., by focusing on searching within an already-parsed announcement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit strategy: 'Directly invoke this tool. Do not pre-check file existence' and suggests using Chinese financial terms. Critical rules specify passing 1-5 keywords and expanding synonyms. Though it doesn't explicitly state when not to use, the guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List available CNINFO A-share announcements for a company (excludes periodic reports).
Args: stock_code: 6-digit stock code, e.g. "000001" category: Optional CNINFO category key, short code, or Chinese label (e.g. "董事会", "DSH", "category_dshgg_szsh") start_date: YYYY-MM-DD inclusive end_date: YYYY-MM-DD inclusive title_keyword: Optional title search keyword limit: Max announcements to return, default 10, max 50
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| category | No | ||
| end_date | No | ||
| start_date | No | ||
| stock_code | Yes | ||
| title_keyword | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that default categories exclude periodic reports and limit max is 50, which is good context. Could mention response format or pagination, but adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with sections: purpose, strategy tag, critical rules, and args. Concise, front-loaded, every sentence valuable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, usage, parameters, and constraints. Has output schema, so return values are documented elsewhere. Complete for a list tool with clear guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but description provides detailed parameter explanations: stock_code format, category examples, date format, title_keyword, limit defaults and max. Adds significant value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it lists CNINFO A-share announcements (excluding periodic reports), and provides guidance to skip if user already has specific date and title, differentiating from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (to discover announce_date/local_key/title before download) and when to skip (if user provides specific date and title, go to keyword_search or download_cninfo_announcement).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up CNINFO orgId mapping for a 6-digit A-share stock code. Diagnostic tool.
Args: stock_code: 6-digit stock code, e.g. "000001" or "600519" refresh: Force re-download of szse_stock.json from CNINFO (default: false)
| Name | Required | Description | Default |
|---|---|---|---|
| refresh | No | ||
| stock_code | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It explains the tool is a diagnostic tool, describes the behavior of the refresh parameter (local cache vs re-download), and implies it is read-only by nature. However, it does not explicitly state that it is non-destructive, but the context suggests it is safe. The description adds sufficient behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with clear sections: main description, strategy, critical rules, and args. Every sentence is useful. It is front-loaded with the core purpose and quickly provides actionable guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, output schema exists), the description covers all necessary aspects: purpose, usage guidelines, parameter details, and behavioral rules. It is complete for an agent to correctly select and invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description provides all parameter semantics. It explains stock_code as '6-digit stock code, e.g. "000001" or "600519"' and refresh as 'Force re-download of szse_stock.json from CNINFO (default: false)'. This adds meaningful examples and behavior beyond the schema's type/title fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Look up CNINFO orgId mapping for a 6-digit A-share stock code. Diagnostic tool.' The verb 'look up' and resource 'CNINFO orgId mapping' are specific. It is distinguished from sibling tools (e.g., download_cninfo_announcement) which deal with announcements, not code mappings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a <strategy> section explicitly stating when to invoke: 'Invoke ONLY when download/list returns "Stock code not found". After success, retry the original list/download call.' It also provides critical rules about preferring refresh=false first. This gives clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Parse a downloaded announcement PDF/HTML into page-split JSON.
Args: local_key: Bundle key returned by list/download, e.g. "000001_DSH_2024-04-30_a1b2c3d4"
| Name | Required | Description | Default |
|---|---|---|---|
| local_key | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It describes the transformation but does not disclose potential side effects, error behavior, or safety (e.g., idempotency, permissions). Adequate but not detailed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a main sentence, a strategy block, and argument explanation. No unnecessary words; front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and an output schema, the description provides enough context for usage and timing. Minor gaps on failure behavior, but overall complete given the simple interface.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema coverage is 0%, but the description explains the parameter local_key as a bundle key with an example. This adds significant meaning beyond the schema's bare type and title.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool parses downloaded announcement PDF/HTML into page-split JSON. It uses a specific verb and resource, and the strategy section distinguishes it from sibling tools like download_cninfo_announcement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The strategy explicitly says to call after a successful download or when JSON is missing. This provides clear when-to-use and when-not-to-use guidance, differentiating from other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a distinct role: listing, downloading, parsing, searching, page retrieval, and stock code lookup. No overlap in functionality, making it easy for an agent to select the correct tool.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., list_cninfo_announcements, download_cninfo_announcement, lookup_stock_code). Minor variation in verb choice ('lookup' vs 'list') is negligible.
With 6 tools covering the core workflow of discovering, downloading, parsing, and searching announcements, the count is well-scoped and avoids bloat. Each tool serves a necessary step in the process.
The tool set covers the primary lifecycle: list, download, parse, keyword search within a document, and page retrieval. Missing a cross-announcement search or category listing, but these are minor gaps that do not break the workflow.
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