Wind MCP
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., "@Wind MCP拉一下贵州茅台过去20天的量价数据"
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.
Wind MCP
A Model Context Protocol server that gives AI assistants direct access to Wind Financial Terminal data.
Wind MCP bridges the Wind Financial Terminal (万得) with AI assistants via the Model Context Protocol. It exposes 23 tools + 1 resource covering market data, fundamentals, macro economics, screening, estimates, portfolio management, and date utilities — all accessible through natural language.
Platform: Windows only (WindPy is a Windows COM-based library).
Companion project to Bloomberg-MCP. Same architecture, same philosophy — but for Wind (万得).
You: "拉一下贵州茅台过去20天的量价数据"
Claude: runs wind_historical with codes=600519.SH, fields=close,volume
→ Returns structured price/volume time series
You: "Find me A-share stocks with PE < 15 and ROE > 20%"
Claude: runs wind_dynamic_screen with universe=A-shares, filters, rank
→ Returns filtered and ranked stock listArchitecture
graph TB
subgraph Clients
CC[Claude Code]
WC[Web Client]
CA[Custom App]
end
subgraph "Wind MCP Server"
direction TB
MCP["FastMCP Server<br/><i>23 tools + 1 resource</i>"]
subgraph Handlers["Handler Layer"]
direction LR
SNAP[Snapshot & Historical]
INTRA[Minute Bars & Ticks]
MACRO[Macro & Sector]
SCREEN[Screening]
EST[Estimates & Holders]
CAL[Calendar & Dates]
end
subgraph Core["Core Layer"]
direction LR
SESSION["WindSession<br/><i>Singleton + atexit</i>"]
CACHE["WindCache<br/><i>TTL-based</i>"]
PARSER["Parser<br/><i>12 parsers</i>"]
CONV["Converter<br/><i>BBG → Wind</i>"]
end
end
WIND["Wind Terminal<br/><i>WindPy (w.start)</i>"]
CC -- stdio --> MCP
WC -- HTTP/SSE --> MCP
CA -- HTTP/SSE --> MCP
MCP --> Handlers
Handlers --> Core
SESSION <--> WIND
style MCP fill:#1a73e8,stroke:#1557b0,color:#fff
style WIND fill:#e65100,stroke:#bf360c,color:#fff
style SESSION fill:#2e7d32,stroke:#1b5e20,color:#fff
style CACHE fill:#2e7d32,stroke:#1b5e20,color:#fffRelated MCP server: Financial Modeling Prep MCP Server
Tools Overview (23 tools + 1 resource)
graph LR
subgraph "Market Data (5)"
T1["wind_snapshot<br/><i>WSS current data</i>"]
T2["wind_historical<br/><i>WSD time series</i>"]
T3["wind_minute_bars<br/><i>WSI candles</i>"]
T4["wind_ticks<br/><i>WST raw ticks</i>"]
T5["wind_realtime<br/><i>WSQ live quotes</i>"]
end
subgraph "Fundamentals & Screening (5)"
T6["wind_dataset<br/><i>WSET tables</i>"]
T7["wind_screen<br/><i>WEQS screens</i>"]
T8["wind_dynamic_screen<br/><i>Custom filter + rank</i>"]
T9["wind_estimates<br/><i>Consensus</i>"]
T10["wind_holders<br/><i>Shareholding</i>"]
end
subgraph "Macro & Sector (3)"
T11["wind_macro<br/><i>EDB series</i>"]
T12["wind_sector_series<br/><i>WSES time series</i>"]
T13["wind_sector_snapshot<br/><i>WSEE snapshot</i>"]
end
subgraph "Connect & Calendar (2)"
T14["wind_stock_connect<br/><i>Northbound flows</i>"]
T15["wind_event_calendar<br/><i>IPO, earnings...</i>"]
end
subgraph "Composite & Portfolio (4)"
T16["wind_company_profile<br/><i>One-stop overview</i>"]
T17["wind_portfolio_report<br/><i>WPF reports</i>"]
T18["wind_portfolio_snapshot<br/><i>WPS holdings</i>"]
T19["wind_portfolio_series<br/><i>WPD time series</i>"]
end
subgraph "Date Utilities (3)"
T20["wind_trading_days<br/><i>TDAYS</i>"]
T21["wind_date_offset<br/><i>TDAYSOFFSET</i>"]
T22["wind_days_count<br/><i>TDAYSCOUNT</i>"]
end
subgraph "Observability (1 tool + 1 resource)"
T23["wind_metrics<br/><i>Server stats</i>"]
R1["wind://health<br/><i>Health check</i>"]
end
style T6 fill:#1a73e8,stroke:#1557b0,color:#fff
style T7 fill:#1a73e8,stroke:#1557b0,color:#fff
style T8 fill:#1a73e8,stroke:#1557b0,color:#fff
style T11 fill:#7b1fa2,stroke:#6a1b9a,color:#fff
style T12 fill:#7b1fa2,stroke:#6a1b9a,color:#fff
style T13 fill:#7b1fa2,stroke:#6a1b9a,color:#fff
style T16 fill:#00695c,stroke:#004d40,color:#fff
style T17 fill:#00695c,stroke:#004d40,color:#fff
style T18 fill:#00695c,stroke:#004d40,color:#fff
style T19 fill:#00695c,stroke:#004d40,color:#fff
style R1 fill:#546e7a,stroke:#37474f,color:#fffKey Features
Bloomberg → Wind Ticker Converter — Accepts Bloomberg-style identifiers (
AAPL US Equity,700 HK Equity,601012 CH Equity) and auto-converts to Wind codes (AAPL.O,00700.HK,601012.SH). Covers US, HK, JP, LN, CH equities, indices, commodities, and currencies.Modular architecture — server.py is a thin entry point. Handlers, models, formatters, parsers, and converters cleanly separated.
16 FieldSet shortcuts — Pre-defined field collections (PRICE, VALUATION, PROFITABILITY, etc.) that expand to Wind field mnemonics.
Cache layer — TTL-based in-memory cache with data-type-aware expiration (30s for realtime, 24h for static).
12 WindPy parsers — Handle WSS, WSD, WSI, WST, WSQ, WSET, EDB, WSES, WSEE, TDAYS, TDAYSOFFSET, TDAYSCOUNT with NaN→None cleanup and column-to-row transposition.
Dynamic Screening — Custom filtering with operators (gt, lt, between, in, eq) + ranking + slicing, without knowing Wind mnemonics.
Data Flow
sequenceDiagram
participant Client as AI Assistant
participant MCP as MCP Server
participant Conv as Ticker Converter
participant Expand as Field Expander
participant Cache as Cache Layer
participant Session as WindSession
participant Wind as Wind Terminal
Client->>MCP: Tool call (JSON)
MCP->>Conv: Convert BBG → Wind codes
Conv-->>MCP: Wind codes
alt FieldSet shortcuts used
MCP->>Expand: Expand FieldSet shortcuts
Expand-->>MCP: Resolved field list
end
MCP->>Cache: Check cache
alt Cache hit
Cache-->>MCP: Cached result
else Cache miss
MCP->>Session: Call WindPy API
Session->>Wind: w.wsd() / w.wss() / ...
Wind-->>Session: WindData response
Session-->>MCP: Parsed dicts
MCP->>Cache: Store with TTL
end
alt Markdown format
MCP-->>Client: Formatted table
else JSON format
MCP-->>Client: Structured JSON
endTool Reference
Market Data
Tool | WindPy API | Description | Key Parameters |
| WSS | Current field values for any security |
|
| WSD | Time series with configurable periodicity |
|
| WSI | OHLCV intraday candles (1/3/5/15/30/60 min) |
|
| WST | Raw tick-level trade data |
|
| WSQ | Live streaming quotes |
|
Fundamentals & Screening
Tool | WindPy API | Description | Key Parameters |
| WSET | Structured datasets (index members, IPOs, etc.) |
|
| WEQS | Execute saved Wind stock screens |
|
| WSS | Custom filter + rank + slice |
|
| WSS | Consensus estimates and target prices |
|
| WSS/WSET | Top holders, institutional, fund holdings |
|
Macro & Sector
Tool | WindPy API | Description | Key Parameters |
| EDB | Macroeconomic indicator time series |
|
| WSES | Sector-level time series |
|
| WSEE | Sector-level current snapshot |
|
Stock Connect & Calendar
Tool | WindPy API | Description | Key Parameters |
| WSET/WSS | Northbound/Southbound flow data |
|
| WSET | IPO, earnings, dividend calendars |
|
Composite & Portfolio
Tool | WindPy API | Description | Key Parameters |
| WSS+WSD | One-stop company overview (snapshot + estimates + price history) |
|
| WPF | Portfolio performance/attribution/risk reports |
|
| WPS | Portfolio NAV, holdings, weights, PnL |
|
| WPD | Portfolio performance time series |
|
Observability
Tool | Type | Description |
| Tool | Server metrics: tool call counts, latency, cache hit rate, uptime |
| Resource | Wind connection and cache health status |
Date Utilities
Tool | WindPy API | Description | Key Parameters |
| TDAYS | List trading days in a range |
|
| TDAYSOFFSET | Offset a date by N trading days |
|
| TDAYSCOUNT | Count trading days between two dates |
|
All tools support response_format: "markdown" (default) or "json".
FieldSet Shortcuts
Instead of remembering Wind field mnemonics, use shorthand names that expand to multiple fields.
FieldSet | Fields | Key Wind Mnemonics |
| 5 | close, open, high, low, pct_chg |
| 4 | pct_chg, pct_chg_5d, pct_chg_1m, pct_chg_ytd |
| 4 | volume, amt, turn, free_turn |
| 5 | pe_ttm, pb_lf, ps_ttm, pcf_ocf_ttm, ev2_to_ebitda |
| 9 | + pe_est, dividend_yield, total_mkt_cap, ev |
| 6 | roe_ttm, roa_ttm, grossprofitmargin, netprofitmargin, operatingprofitmargin, roic |
| 4 | yoyprofit, yoyrevenue, yoyocf, qfa_yoygr |
| 6 | debttoassets, current_ratio, quick_ratio, cashflow_to_debt, longdebttodebt, equity_ratio |
| 5 | ocfps, fcf, cf_from_ops, capex, dividendps |
| 5 | rsi, macd, vol_20d, beta_100w, atr_14d |
| 4 | rating_avg, est_target_price, est_eps_fy1, est_net_profit_fy1 |
| 4 | est_eps_chg_4w, est_eps_chg_13w, est_num_up, est_num_down |
| 2 | industry_sw, industry_sw_lv2 |
| 4 | sh_hk_share_pct, sh_hk_share_chg, sh_hk_share_amt, sh_hk_share_rank |
| 3 | margin_buy_bal, margin_sell_bal, margin_net_bal |
| 5 | beta_100w, volatility_20d, volatility_60d, sharpe_20d, max_drawdown_1y |
| 50+ | All of the above combined |
Bloomberg → Wind Ticker Conversion
Wind MCP automatically converts Bloomberg-style identifiers to Wind codes:
Bloomberg Format | Wind Code | Market |
|
| US (NASDAQ) |
|
| US (NYSE) |
|
| Hong Kong |
|
| Japan |
|
| London |
|
| A-share (Shanghai) |
|
| A-share (Shenzhen) |
|
| A-share (ChiNext) |
|
| Index |
|
| Index |
|
| Commodity |
|
| Currency |
|
| Passthrough (already Wind) |
Dynamic Screening
Build custom screens with pre-validated field sets, filters, and ranking — no need to know Wind field mnemonics.
flowchart LR
A["Universe<br/><i>index, sector,<br/>or ticker list</i>"] --> B["Field Expansion<br/><i>FieldSet shortcuts<br/>→ Wind fields</i>"]
B --> C["Wind API<br/><i>WSS request</i>"]
C --> D["Filter<br/><i>gt, lt, between,<br/>in, eq, ...</i>"]
D --> E["Rank & Slice<br/><i>rank_by + top_n</i>"]
E --> F["Response<br/><i>Markdown table<br/>or JSON</i>"]
style A fill:#e8f5e9,stroke:#2e7d32
style C fill:#fff3e0,stroke:#ff6f00
style F fill:#e3f2fd,stroke:#1a73e8Filter Operators
Operator | Description | Example |
| Greater than (or equal) |
|
| Less than (or equal) |
|
| Equals / not equals |
|
| Range (inclusive) |
|
| Value in list |
|
Example: Find Undervalued High-ROE A-shares
{
"universe": "sector:全部A股",
"fields": ["PRICE", "VALUATION", "PROFITABILITY"],
"filters": [
{"field": "pe_ttm", "op": "lt", "value": 15},
{"field": "roe_ttm", "op": "gt", "value": 20}
],
"rank_by": "roe_ttm",
"rank_descending": true,
"top_n": 20
}Cache Layer
Built-in cache reduces Wind API load with data-type-aware TTLs:
Data Type | Default TTL | Rationale |
Realtime quotes (WSQ) | 30 seconds | Near real-time |
Snapshot (WSS) | 5 minutes | Moderate refresh |
Minute bars (WSI) | 5 minutes | Intraday refresh |
Ticks (WST) | 1 minute | High-frequency |
Portfolio (WPF/WPS/WPD) | 5 minutes | Moderate refresh |
Screening (WEQS) | 10 minutes | Moderate refresh |
Estimates | 4 hours | Consensus updates infrequently |
Historical (WSD) | 12 hours | End-of-day data stable |
Macro data (EDB) | 24 hours | Periodic updates |
Dataset (WSET) | 24 hours | Reference data |
Sector (WSES/WSEE) | 24 hours | Rarely changes |
Holders / Dates | 24 hours | Rarely changes |
All TTLs are configurable via wind_mcp.toml or environment variables. See Configuration.
Configuration
Server behavior can be customized via wind_mcp.toml in the project root. All values can also be overridden via environment variables with the prefix WIND_MCP_ (e.g., WIND_MCP_CACHE_MAXSIZE=5000).
[session]
connect_timeout = 30 # Seconds to wait for Wind connection
reconnect_retries = 3 # Auto-reconnect attempts
reconnect_backoff = 1.0 # Backoff multiplier between retries
[cache]
maxsize = 2000 # Max cached entries (LRU eviction)
ttl_realtime = 30 # WSQ — near real-time
ttl_snapshot = 300 # WSS — 5 min
ttl_historical = 43200 # WSD — 12 hours
ttl_dataset = 86400 # WSET — 24 hours
ttl_macro = 86400 # EDB — 24 hours
ttl_portfolio = 300 # WPF/WPS/WPD — 5 min
[api]
timeout = 30.0 # Per-call timeout (seconds)
retries = 2 # Retry count on transient errors
backoff = 1.0 # Backoff multiplier
[log]
format = "text" # "json" or "text"
level = "INFO"Project Structure
wind-mcp/
├── pyproject.toml
├── README.md
├── LICENSE
├── wind_mcp.toml # Server configuration (cache TTL, timeouts, retries)
├── run_server.bat / run_server.ps1
├── src/wind_mcp/
│ ├── __init__.py
│ ├── __main__.py
│ ├── server.py # FastMCP entry point, 23 tools + 1 resource
│ ├── formatters.py # Markdown table & JSON output
│ ├── core/
│ │ ├── session.py # WindSession singleton + reconnect
│ │ ├── cache.py # TTL cache with LRU eviction
│ │ ├── parser.py # 12 WindData parsers
│ │ ├── converter.py # Bloomberg → Wind ticker converter
│ │ ├── executor.py # Single-thread executor for Wind API calls
│ │ ├── validators.py # Input validation (codes, dates, fields)
│ │ ├── resilience.py # Timeout, retry, stale-cache fallback
│ │ ├── config.py # Centralized config (env > toml > defaults)
│ │ ├── metrics.py # Counters + histograms for observability
│ │ ├── universe.py # Universe resolution (index/sector → security list)
│ │ └── filters.py # In-memory data filtering (gt/lt/between/in/...)
│ ├── models/
│ │ ├── enums.py # ResponseFormat, Periodicity, etc.
│ │ └── inputs.py # Pydantic input models
│ ├── tools/
│ │ ├── fieldsets.py # 16 FieldSet definitions
│ │ └── field_expander.py # FieldSet → field list resolver
│ ├── handlers/ # 16 handler modules
│ │ ├── snapshot.py # WSS
│ │ ├── historical.py # WSD
│ │ ├── minute_bars.py # WSI
│ │ ├── ticks.py # WST
│ │ ├── realtime.py # WSQ
│ │ ├── dataset.py # WSET
│ │ ├── macro.py # EDB
│ │ ├── sector.py # WSES / WSEE
│ │ ├── screening.py # WEQS + dynamic screen
│ │ ├── estimates.py # Consensus estimates
│ │ ├── holders.py # Holder analysis
│ │ ├── stock_connect.py # Northbound/Southbound
│ │ ├── calendar.py # Event calendar
│ │ ├── dates.py # TDAYS / TDAYSOFFSET / TDAYSCOUNT
│ │ ├── composite.py # Company profile (multi-source)
│ │ └── portfolio.py # WPF / WPS / WPD portfolio tools
│ └── data/ # Static mapping files
│ ├── us_exchange_map.json # ~200 US tickers → exchange
│ ├── index_map.json # BBG → Wind index mapping
│ ├── commodity_map.json # BBG → Wind commodity mapping
│ └── currency_map.json # BBG → Wind currency mapping
├── tests/ # 101 unit tests (no Wind needed)
│ ├── test_parser.py
│ ├── test_fieldsets.py
│ ├── test_cache.py
│ ├── test_config.py
│ ├── test_converter.py
│ ├── test_filters.py
│ ├── test_formatters.py
│ ├── test_inputs.py
│ ├── test_metrics.py
│ ├── test_resilience.py
│ └── test_validators.py
└── examples/
├── basic_usage.py
└── screening_example.pyInstallation
Prerequisites
Windows (WindPy is a Windows COM-based library)
Python 3.10+ (must match Wind Terminal bitness — typically 64-bit)
Wind Financial Terminal (万得) running and logged in (iWind or WFT)
WindPy — installed via Wind Terminal, NOT via pip. Open Wind Terminal → click "Repair" or install the WindPy plugin from the Tools menu.
Verify WindPy
Before installing Wind MCP, verify that WindPy works in your Python environment:
python -c "from WindPy import w; w.start(); print(w.isconnected())"
# Should print: TrueIf this returns False or errors, fix your WindPy installation first. Common issues:
Wind Terminal not running / not logged in
Python bitness mismatch (e.g., 32-bit Python with 64-bit Wind)
WindPy not installed (repair via Wind Terminal)
Install
# From source
git clone https://github.com/QmQsun/Wind-MCP.git
cd Wind-MCP
pip install . # standard install
# or: pip install -e . # editable mode (for development)
# Or from PyPI
pip install wind-mcpConfigure Claude Code
Add a .mcp.json file to register the MCP server. Two options:
Global (all projects can use Wind MCP) — place at ~/.mcp.json (i.e., C:\Users\<you>\.mcp.json):
{
"mcpServers": {
"wind-mcp": {
"command": "wind-mcp",
"args": []
}
}
}Project-level (only one project) — place .mcp.json in the project root directory.
The
wind-mcpcommand is the console entry point installed by pip. Alternatively, use"command": "python", "args": ["-m", "wind_mcp.server"].
Quick Start
As an MCP Server
# stdio (default — for Claude Code)
python -m wind_mcp.server
# HTTP transport (for web clients)
python -m wind_mcp.server --http --port=8080
# SSE transport (for streaming clients)
python -m wind_mcp.server --sse --port=8080Windows
# Command Prompt
run_server.bat
# PowerShell
.\run_server.ps1Wind API Coverage
graph LR
subgraph "Wind Terminal (WindPy)"
WSS["WSS<br/><i>Snapshot</i>"]
WSD["WSD<br/><i>Historical</i>"]
WSI["WSI<br/><i>Minute bars</i>"]
WST["WST<br/><i>Tick data</i>"]
WSQ["WSQ<br/><i>Realtime</i>"]
WSET["WSET<br/><i>Datasets</i>"]
EDB["EDB<br/><i>Macro</i>"]
WSES["WSES<br/><i>Sector series</i>"]
WSEE["WSEE<br/><i>Sector snapshot</i>"]
WEQS["WEQS<br/><i>Stock screener</i>"]
TDAYS["TDAYS<br/><i>Trading days</i>"]
end
WSS --- T1[Snapshot + Estimates + Holders + Screen]
WSD --- T2[Historical Time Series]
WSI --- T3[Intraday Bars]
WST --- T4[Tick Data]
WSQ --- T5[Realtime Quotes]
WSET --- T6[Datasets + Connect + Calendar]
EDB --- T7[Macro Indicators]
WSES --- T8[Sector Time Series]
WSEE --- T9[Sector Snapshot]
WEQS --- T10[Saved Screens]
TDAYS --- T11[Date Utilities]
style WSS fill:#1a73e8,stroke:#1557b0,color:#fff
style WSD fill:#1a73e8,stroke:#1557b0,color:#fff
style EDB fill:#7b1fa2,stroke:#6a1b9a,color:#fff
style WEQS fill:#e65100,stroke:#bf360c,color:#fffContributing
Contributions welcome! Please open an issue or submit a pull request.
pip install -e ".[dev]"
pytest # Unit tests (no Wind Terminal needed)
black src/ tests/
ruff check src/ tests/Related Projects
Bloomberg-MCP — Same architecture for Bloomberg Terminal
License
MIT — see LICENSE for details.
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