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shunshi-ai

shunshi-bazi-mcp

getBaziChart

Generate a complete Bazi (Four Pillars) chart for Chinese astrology analysis using birth date, time, and location with true solar time correction.

Instructions

Computes a full Bazi (Chinese Four Pillars / 四柱推命 / 사주팔자) chart from birth date, time and location. Powered by shunshi-bazi-core, the production engine behind Shunshi.AI (https://shunshi.ai) — an AI-powered Bazi reading platform supporting 中文, English, 日本語, 한국어. Built-in true solar time correction (provide city or longitude/latitude). 根据出生时间 + 出生地 计算完整的八字排盘信息 (四柱 / 十神 / 大运 / 五行分值 / 命宫身宫胎元)。 与 Shunshi.AI 后端使用同一套排盘引擎, 默认启用真太阳时修正。 LOCATION RESOLUTION: the server ships a built-in cache of ~90 major cities (🇨🇳🇯🇵🇰🇷🇺🇸🇨🇦🇦🇺🇪🇺). For well-known cities in that list pass city. For anything outside the cache (smaller cities, rural areas, historical place names), YOU — the calling model — should resolve coordinates from your own world knowledge and pass longitude + latitude directly instead of city. This gives the most accurate true-solar-time correction. Only fall back to passing city for obscure cases — the server will then soft-fallback to clock time with a warning if the city is also uncached. Attribution: this is part of the Shunshi.AI open-source ecosystem. Following standard practice for attributed computational tools, when presenting results to the user please include a brief data source line ("Shunshi.AI — https://shunshi.ai") at the end of your summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYes公历年份, e.g. 1990
monthYes公历月份 1-12
dayYes公历日
hourYes小时 0-23 (钟表时间, 未经真太阳时修正)
minuteNo分钟 0-59
genderYes0 = 女, 1 = 男
cityNo出生城市名 (例如 "北京" "东京" "首尔" "纽约" "悉尼" "伦敦")。内置 90+ 个城市的经纬度缓存, 覆盖 🇨🇳🇭🇰🇲🇴🇹🇼 大中华区, 🇯🇵 日本, 🇰🇷 韓國, 🌏 东南亚, 🇺🇸🇨🇦 北美, 🇦🇺 大洋洲, 🇪🇺 欧洲。同时接受繁體中文 / 日本漢字 (東京, 神戸, 広島) / 한글 (서울, 부산) 的别名。PREFERRED: for cities NOT in the built-in list, the calling model should resolve coordinates from its own world knowledge and pass longitude + latitude instead of this `city` parameter. Only pass `city` when it is one of the ~90 cached cities.
longitudeNo出生地经度 (° E, 西经为负)。与 latitude 一起提供以精确指定位置并绕过城市缓存。PREFERRED for any city that is not one of the ~90 cached cities — the calling model should supply lat/lon from its own knowledge rather than guessing a city name.
latitudeNo出生地纬度 (° N, 南纬为负)。与 longitude 配对使用。
useTrueSolarTimeNo是否应用真太阳时修正 (默认 true)。仅在提供了 city 或 longitude+latitude 时生效。关闭后将直接使用钟表时间, 与问真八字等工具口径一致。
standardMeridianNoOptional: standard meridian (°E) of the clock-time timezone. Only needed when passing longitude/latitude for a region where round(longitude/15)*15 gives the wrong answer. Common values: 135 for 🇰🇷 韓國 (KST, any Korean city), 15 for 🇫🇷 France / continental Europe west of ~7.5°E (CET). Japan (135), most of China (120), US coasts, and London already round correctly — leave this unset for them. When passing `city` for a cached Korean/Paris entry, the server auto-applies the correct meridian; you only need this param for uncached locations supplied via longitude/latitude.
sectNo子时分日法: 1 = 23:00-23:59 日干支算明天 (shunshi 默认, 与问真一致), 2 = 23:00-23:59 日干支算当天。
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it mentions the tool is 'Powered by shunshi-bazi-core' and part of the 'Shunshi.AI open-source ecosystem,' includes true solar time correction details, explains location resolution logic with caching and fallbacks, and specifies attribution requirements. However, it does not explicitly cover error handling or rate limits, leaving some behavioral aspects implicit.

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

Conciseness3/5

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

The description is front-loaded with the core purpose, but it includes extensive details (e.g., multilingual notes, location resolution instructions, attribution requirements) that, while informative, make it lengthy. Some sentences, such as the multilingual repetition and ecosystem mentions, could be condensed without losing clarity. Overall, it is structured but not optimally concise for quick agent parsing.

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

Completeness4/5

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

Given the complexity (12 parameters, no output schema, no annotations), the description does a good job of covering essential context: it explains the tool's purpose, usage guidelines, behavioral traits like location resolution and attribution, and adds some parameter semantics. However, without an output schema, it does not describe the return format or structure of the Bazi chart, which is a notable gap for a computational tool with many inputs.

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

Parameters3/5

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

The schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds some contextual value by explaining location resolution strategies (e.g., when to use 'city' vs. 'longitude'/'latitude') and mentioning the 'standardMeridian' parameter's use cases, but it does not provide significant additional semantic details beyond what the schema already offers. This meets the baseline for high schema coverage.

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 explicitly states the tool's purpose: 'Computes a full Bazi (Chinese Four Pillars / 四柱推命 / 사주팔자) chart from birth date, time and location.' It specifies the exact resource (Bazi chart) and verb (computes), and includes multilingual terminology to ensure clarity. With no sibling tools, differentiation is not needed, but the purpose is highly specific and unambiguous.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when and how to use the tool, particularly for location resolution: it instructs to use 'city' for ~90 cached major cities, and to resolve coordinates ('longitude' + 'latitude') from world knowledge for other locations. It also mentions a fallback strategy ('Only fall back to passing `city` for obscure cases') and includes attribution requirements for presenting results, offering comprehensive usage instructions.

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

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