What can you do with this server?

This server provides tools to fetch and analyze Chinese A-share (沪深京) financial statements, enabling LLMs to query structured financial data from East Money (东方财富) via akshare — free, with no API token required. * get_three_statements — Retrieve the three core annual financial statements (balance sheet, income statement, cash flow statement) for any A-share listed company by stock code and year. Supports multiple code formats (e.g., 000001, SZ000001, 000001.SZ). Returns ~150 curated fields in CNY with in-memory caching. * cross_check_balance — Run financial consistency checks to verify internal coherence across the three statements, including: balance sheet equation, cash flow identity, and beginning/ending cash reconciliation. Returns pass/fail/skipped status and numerical differences (with a 10,000 CNY rounding tolerance). * compare_peers — Compare up to ~10 companies side-by-side for a given year on key metrics (total assets, revenue, net profit, operating cash flow, equity, derived ROE), with rankings, summary statistics (max/min/avg/std dev), and concurrent fetching for speed. * track_company_history (HTTP API) — View cross-year trends with YoY growth, CAGR, and anomaly detection. * parse_document (optional, requires [pdf] extra) — Convert PDF/DOCX/PPTX/images to LLM-ready markdown using MinerU.

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ashare-mcp

by yli769227-jpg

Overview Schema Related Servers Score Discussions

Python

Remote

ashare-mcp

Turn A-share financial reports into tools your LLM can call. An MCP server that turns Chinese A-share financial statements into tools your LLM can call.

Let Claude (or any MCP client) get structured balance sheets, income statements, and cash flow statements with a single prompt like "How was Ping An Bank's 2024 annual report?" Fields are curated, units are clear, and it is cache-friendly.

Data sourced from East Money via akshare, completely free, no token required.

Why build another one?

Most "Financial LLM" projects on GitHub are crowded in trading agents and SEC 10-K RAG—the former is highly homogenized, and the latter only serves US stocks. The combination of A-shares + Chinese + MCP protocol layer is almost a blank space.

ashare-mcp has a very narrow focus: do one thing—A-share financial reports—and do it well enough to be integrated into any LLM client in ten seconds. It doesn't predict stock prices, write research reports, or make decisions for you—it simply moves data from East Money into LLM tool calls, with clean fields, clear units, and explicit error handling.

Related MCP server: sfc-data-mcp

Quick Start

git clone https://github.com/yli769227-jpg/ashare-mcp.git
cd ashare-mcp
python3 -m venv .venv && source .venv/bin/activate
pip install -e .

Run a smoke test:

python -c "from ashare_mcp.data_source import get_annual_statements; \
  r = get_annual_statements('SZ000001', 2024); \
  print(r['company_name'], r['balance_sheet']['TOTAL_ASSETS'])"
# -> 平安银行 5769270000000.0

Integrate with Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (Mac):

{
  "mcpServers": {
    "ashare": {
      "command": "/absolute/path/to/ashare-mcp/.venv/bin/python",
      "args": ["-m", "ashare_mcp.server"]
    }
  }
}

Restart Claude Desktop, and you can ask directly:

Help me look at Ping An Bank's 2024 annual report. What are the total assets, total liabilities, net profit, and operating cash flow?

Tool List

Tool	Input	Output
`get_three_statements`	`stock_code`, `year`	Three major annual statements (curated ~150 fields)
`cross_check_balance`	`stock_code`, `year`	3 cross-check results + variance + industry-specific
`compare_peers`	`stock_codes[]`, `year`, `metrics?`	Peer comparison for N companies + ranking / max-min-avg-std + ROE

Code normalization supports multiple formats: 000001 / SZ000001 / sz.000001 / 000001.SZ.

cross_check_balance currently includes 4 cross-checks (the first 3 are industry-agnostic, the 4th is industry-aware):

Balance Sheet Equilibrium — TOTAL_ASSETS = TOTAL_LIABILITIES + TOTAL_EQUITY
Cash Flow Identity — NETCASH_OPERATE + NETCASH_INVEST + NETCASH_FINANCE + RATE_CHANGE_EFFECT = CCE_ADD
End/Beginning Cash Reconciliation — END_CCE − BEGIN_CCE = CCE_ADD
Operating Profit Decomposition (Industry-Aware)
- Banks: OPERATE_PROFIT = OPERATE_INCOME − OPERATE_EXPENSE
- Industrial/Commercial: OPERATE_PROFIT = TOTAL_OPERATE_INCOME − TOTAL_OPERATE_COST + OTHER_INCOME + INVEST_INCOME + FAIRVALUE_CHANGE_INCOME + ASSET_IMPAIRMENT_INCOME + CREDIT_IMPAIRMENT_INCOME + ASSET_DISPOSAL_INCOME [+ EXCHANGE_INCOME]
- Automatic Industry Identification: If ACCEPT_DEPOSIT > 1 billion, use the bank formula; if TOTAL_OPERATE_INCOME + TOTAL_OPERATE_COST exist, use the industrial formula; otherwise skipped (insurance, etc., not yet supported).

Tolerance: 10,000 RMB for the first 3 (rounding of individual items), 10 million RMB for the 4th (cumulative rounding of multiple items). If fields are missing or the industry cannot be identified, the check is skipped, which does not affect other checks. Tested on 3 industries (banks / liquor / batteries) and 4 companies' 2024 annual reports, all passed 4/4.

Leverages LRU cache: Call get_three_statements first, then cross_check_balance, and the latter returns in < 1ms (data for the same stock is already in memory).

compare_peers default metrics: TOTAL_ASSETS / TOTAL_OPERATE_INCOME / PARENT_NETPROFIT / NETCASH_OPERATE / TOTAL_EQUITY, automatically derives ROE = PARENT_NETPROFIT / Average Equity (average of current year-end equity and last year-end equity; last year's data is retrieved via LRU cache at almost zero cost; if last year's data is missing, it falls back to year-end equity, marked in the roe_method field as ending_equity_fallback). Automatic fallback: For banks, if TOTAL_OPERATE_INCOME is missing, it falls back to OPERATE_INCOME and marks it in the fallbacks field. Concurrency implementation: ThreadPoolExecutor (max_workers=8), pulls data for N companies in parallel (failure of one does not crash the whole process, recorded in errors). Tested 4 major banks' 2024 annual reports in ~38s; China Merchants Bank ROE 12.85% (long-term leader in retail).

Architecture

flowchart LR
    LLM[Claude / 任意 MCP 客户端] -->|JSON-RPC over stdio| Server[ashare-mcp<br/>FastMCP server]
    Server -->|代码归一化| Norm[股票代码归一化<br/>SZ/SH/BJ 自动判断]
    Server -->|拉取三表| DS[数据源封装<br/>akshare 包装层]
    DS -->|缓存命中| Cache[(进程内存缓存<br/>lru_cache)]
    DS -->|缓存未命中| YearlyEM[akshare<br/>by_yearly_em]
    YearlyEM -->|HTTP| EM[东方财富<br/>财报数据接口]
    DS -->|字段过滤| Filter[剔除元数据列<br/>剔除同比列<br/>剔除空/零字段]
    Server -->|结构化 JSON| LLM

Key Design:

Field names retain original East Money English (TOTAL_ASSETS / LOAN_ADVANCE / NETPROFIT). LLMs can understand them directly, and fields for different industries like banks / industrial / insurance are all in the same dictionary, requiring no industry judgment.
In-process memory caching makes "multi-year comparison for the same company" almost zero-cost—cold start pulls the full volume, subsequent year switching is < 1ms.
Logs go to stderr, not polluting the MCP stdio protocol channel.

Roadmap

Version	Tool	Status
v0	`get_three_statements`	✅
v1	`cross_check_balance` (3 industry-agnostic checks)	✅
v1	`compare_peers` (peer comparison + ROE derivation)	✅
v1.5 (Current)	`cross_check_balance` + Operating Profit Decomposition (Industry-aware: banks / industrial)	✅
v1.5 (Current)	`compare_peers` upgraded to ROE_avg (average equity)	✅
v2	Year-over-year trend tool `track_company_history` (single company multi-year + CAGR)	Pending
v2	Quarterly data + YoY/QoQ derived metrics	Pending
v2	Official MCP registry release	Pending

Local Development

# 增量验证(每次改完跑一遍)
python -c "from ashare_mcp.utils import normalize_stock_code; \
  assert normalize_stock_code('000001') == 'SZ000001'"

python -c "from ashare_mcp.server import mcp; \
  import asyncio; print([t.name for t in asyncio.run(mcp.list_tools())])"

Data Disclaimer

Data source: East Money, via akshare.
Data latency, definitions, and accuracy are subject to East Money, and do not constitute investment advice.
For educational and research purposes only.

License

MIT — see LICENSE.

Available Tools

3 tools

compare_peersA

同业 N 家公司同年年报横向对比,自动算排名 / 最大最小 / 均值 / 标准差,加派生指标 ROE。

参数: stock_codes: 公司代码列表,如 ['000001', '600036', '601398']。建议 2-10 家。支持各种格式:'000001' / 'SZ000001' / 'sz.000001' / '000001.SZ'。 year: 年份。 metrics: 可选,自定义对比字段。默认包括: TOTAL_ASSETS / TOTAL_OPERATE_INCOME / PARENT_NETPROFIT / NETCASH_OPERATE / TOTAL_EQUITY。派生指标 ROE = PARENT_NETPROFIT / TOTAL_EQUITY 总是会算上。银行业 TOTAL_OPERATE_INCOME 缺失时自动 fallback 到 OPERATE_INCOME(在 fallbacks 字段里标注)。

返回: { "year": 2024, "report_date": "2024-12-31", "metrics": ["TOTAL_ASSETS", ..., "ROE"], "companies": [ { "stock_code": "SZ000001", "company_name": "平安银行", "values": {metric: number}, "ranks": {metric: rank}, # 1 = 最大 "fallbacks": {original_key: actual_key} | null } ], "summary": { metric: {"max", "min", "avg", "std", "count"} }, "errors": [ {"stock_code": "...", "error": "..."} # 单家失败不挂整体 ] }

并发实现: ThreadPoolExecutor(max_workers=8),N 家公司并行拉。缓存联动: 已经查过的公司走 lru cache,< 1ms 复用。

ParametersJSON Schema

Name	Required	Description	Default
`stock_codes`	Yes
`year`	Yes
`metrics`	No

TDQS

A4.8/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses concurrency (ThreadPoolExecutor with 8 workers), caching (lru cache), single-failure tolerance, and fallback logic for bank metrics. Return structure is detailed with example JSON.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections for parameters, return fields, and implementation details. Every sentence adds value without redundancy. Length is appropriate for a complex tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, yet description provides complete return structure with example JSON, concurrency, caching, and error handling. Covers all behavioral aspects needed for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 0% coverage, but description fully explains stock_codes formats, year, metrics default and optional, and derived ROE. Provides examples and constraints, compensating completely for schema gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it compares annual reports of N peer companies horizontally, computes ranks, min/max, mean, std, and derived ROE. It distinguishes from siblings like cross_check_balance and get_three_statements by specifying peer comparison logic.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly recommends 2-10 companies, describes default metrics, and explains derived ROE always included. It doesn't explicitly state when not to use or alternatives, but provides clear context for appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

cross_check_balanceA

跑财务勾稽校验,检测三大表数据是否互相自洽。返回每条校验的 passed/failed/skipped 状态与误差。

参数: stock_code: A 股代码,支持多种格式(同 get_three_statements)。 year: 年份,如 2024。仅支持年报。

返回: { "stock_code": "SZ000001", "company_name": "平安银行", "report_date": "2024-12-31", "checks": [ { "name": "balance_sheet_equation", "label": "资产负债平衡", "formula": "TOTAL_ASSETS = TOTAL_LIABILITIES + TOTAL_EQUITY", "lhs_value": 5769270000000.0, "rhs_value": 5769270000000.0, "diff": 0.0, "tolerance": 10000.0, "status": "passed" }, ... ], "summary": {"total": 3, "passed": 3, "failed": 0, "skipped": 0} }

当前 v1 包含 3 条行业通用勾稽:

资产负债平衡: TOTAL_ASSETS = TOTAL_LIABILITIES + TOTAL_EQUITY
现金流恒等式: 三大现金流 + 汇率影响 = 现金净增加额
期末/期初现金对账: END_CCE - BEGIN_CCE = CCE_ADD

容忍度 1 万元(财报舍入)。字段缺失时该条 status='skipped',不影响其它校验。

ParametersJSON Schema

Name	Required	Description	Default
`stock_code`	Yes
`year`	Yes

TDQS

A4.8/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations given, but description fully covers behavior: checks three specific equations with tolerance, returns passed/failed/skipped status, handles missing fields gracefully, and notes annual-only support.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with intro, parameter details, return format example, and list of checks. Every sentence is informative and no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, description provides full return example and explains all statuses and tolerance. Parameter semantics are fully covered, and sibling references add context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds significant meaning beyond schema: explains stock_code format and links to sibling tool, clarifies year only supports annual reports, and includes example values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool performs financial cross-check validation among three statements, distinguishing it from siblings like get_three_statements and compare_peers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides parameter specifics (stock_code supports multiple formats, year only annual reports) and lists the three checks. Does not explicitly exclude use cases, but context suffices.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_three_statementsA

拉取 A 股某只股票某年的年报三大财务报表(资产负债表 / 利润表 / 现金流量表)。

参数: stock_code: A 股代码,支持多种格式 —— '000001' / 'SZ000001' / 'sz.000001' / '000001.SZ'。 year: 年份(整数),如 2024。仅支持年报(报告期 12-31)。

返回: { "stock_code": "SZ000001", "company_name": "平安银行", "report_date": "2024-12-31", "currency": "CNY", "unit": "yuan (元)", "balance_sheet": {...}, # 字段如 TOTAL_ASSETS / LOAN_ADVANCE / ACCEPT_DEPOSIT "income_statement": {...}, # 字段如 OPERATE_INCOME / NETPROFIT / PARENT_NETPROFIT "cash_flow_statement": {...}, # 字段如 NETCASH_OPERATE / NETCASH_INVEST / NETCASH_FINANCE }

数据源: 东方财富(via akshare)。字段名为东方财富原始英文(SCREAMING_SNAKE_CASE)。单位: 人民币元。缓存: 进程内存缓存,同一只股票多次查询(不同年份)只走一次网络。

ParametersJSON Schema

Name	Required	Description	Default
`stock_code`	Yes
`year`	Yes

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full weight. It discloses caching behavior, data source, currency, unit, and field naming conventions, but does not mention authentication or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a brief intro, bullet points for parameters, and a clear return format, though it could be slightly more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and lack of output schema, the description covers all relevant aspects: purpose, parameters, return structure, data source, caching, and units, making it fully informative.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description adds crucial detail: multiple accepted formats for stock_code and the requirement that year be an integer for annual reports only.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb (拉取/fetch), resource (年报三大财务报表), and scope (A股某只股票某年), distinguishing it from sibling tools like compare_peers and cross_check_balance.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description specifies that only annual reports are supported and provides parameter formats, but does not explicitly compare to sibling tools or state when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

3 tool updatesv0.1.0Apr 28, 2026
- First observedcompare_peers
- First observedcross_check_balance
- First observedget_three_statements

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clear, distinct purpose: retrieving financial statements, cross-checking consistency, and comparing peers. There is no overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (compare_peers, cross_check_balance, get_three_statements), making them predictable and clear.

Tool Count4/5

Three tools is minimal but sufficient for the focused domain of A-share annual financial analysis. The count feels well-scoped without being overly thin.

Completeness4/5

The tools cover core workflows: data retrieval, internal consistency checks, and peer comparison. Minor gaps like quarterly data or individual ratio lookups exist, but the surface is largely complete for annual report analysis.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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