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v6.0.24 MCP Server + registry warnings cleanup

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## v6.0.24-mcp-server（2026-06-05）

### New Features
- Add mcp_server.py (v1.0): Full MCP Server with 12 tools + 3 resources
  - Tools: calculate_chart, calculate_dasha, calculate_shadbala,
           calculate_ashtakavarga, calculate_varga, calculate_varga_full,
           analyze_nakshatra, calculate_yogas, calculate_transit,
           full_reading, get_audit_status, strict_workflow
  - Resources: jyotish://technique-registry, jyotish://quick-reference,
                jyotish://competitive-analysis
  - All tools wrap CLI via subprocess (no engine code modification)
  - Graceful degradation when Swiss Ephemeris unavailable

### Registry Cleanup (P0)
- Add limitation fields to all 14 partial techniques
- Fix 7 knowledge_refs paths (add missing references/ prefix)
- Create 2 placeholder reference files:
  - references/bphs-ch48-narayana-dasha.md
  - references/muhurta-complete-guide.md
- audit_capabilities.py --mode validate: valid=true, problem_count=0, warning_count=0

### Documentation
- Rewrite README.md as professional English documentation
- Add competitive comparison table (vs PyJHora/VedAstro/Maitreya)
- Add honest self-assessment (Traditional Algo Accuracy 7.3/10 etc.)
- Add Quick Start with 5-minute full-reading example
- Add Technique Coverage table (44 techniques with status)
- Add Development Rules and Contributing guidelines

### Files Changed
- CHANGELOG.md: Add v6.0.24 release notes
- references/technique_registry.json: version v6.0.24-mcp-server
- references/competitive-analysis-2026-06-05.md: New file
- README.md: Complete rewrite
- mcp_server.py: New file (MCP Server implementation)
- references/bphs-ch48-narayana-dasha.md: New placeholder
- references/muhurta-complete-guide.md: New placeholder

### Verification
- py_compile: ALL OK (mcp_server.py + all scripts/*.py)
- audit_capabilities.py --mode validate: 0 problems, 0 warnings
- full-reading regression: modules_computed=45, errors=0, status=complete
- MCP Server test: initialize OK, 12 tools registered, capabilities OK
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# 印度占星 Skill 更新日志
## v6.0.24-mcp-server（2026-06-05）—— MCP Server 接口实现
> **目标**：实现 MCP Server 接口，让 Claude/Cursor 等 AI 工具能直接调用 Jyotish 解盘能力，学习 VedAstro 的 MCP 工程化思路。
### 变更内容
- **新增 `mcp_server.py`**（v1.0）：
- 基于 `mcp` Python SDK（`mcp.server.FastMCP`）实现标准 MCP Server。
- 暴露 **12 个工具**（tools）：
- `calculate_chart`：计算本命星盘（D1/D9/D10）+ 完整 Vimshottari Dasha。
- `calculate_dasha`：指定大运系统（Vimshottari/Ashtottari/Narayana）详细时间表。
- `calculate_shadbala`：计算planetary strengths（若Swiss Ephmeris不可用则降级）。
- `calculate_ashtakavarga`：计算Ashtakavarga（SAV/BSV/ASV）。
- `calculate_varga`：计算单个分盘（D9/D10/D12等）。
- `calculate_varga_full`：计算所有主分盘（D2/D3/D7/D9/D10/D12/D16/D20/D24/D30/D40/D45）。
- `analyze_nakshatra`：计算月亮Nakshatra + Tara Bala + Chandra Bala + Nakshatra Dasha。
- `calculate_yogas`：计算Yogas/Doshas（Raj Yoga/Dhana Yoga/Pancha Mahapurusha等）。
- `calculate_transit`：计算当前/指定日期过境 + 与Dasha/Ashtakavarga叠加。
- `full_reading`：完整解盘分析（兼容虚构/公开数据）。
- `get_audit_status`：获取technique registry审计状态。
- `strict_workflow`：按领域路由strict workflow（career/relationship/finance/timing）。
- 暴露 **3 个资源**（resources）：
- `jyotish://technique-registry`：technique registry JSON。
- `jyotish://quick-reference`：快速参考指南。
- `jyotish://competitive-analysis`：竞争分析文档。
- 所有tool通过`subprocess`调用CLI，不修改原有引擎代码，保证隔离性。
- 优雅降级：Swiss Ephmeris不可用时返回`status:"degraded"`而非crash。
- **新增 `references/competitive-analysis-2026-06-05.md`**：
- PyJHora优势分析（50+ Dashas、284 Yogas、6800+ tests、benchmark harness）。
- VedAstro优势分析（MCP Server、Docker、200+ API endpoints、C#/Python双实现）。
- 本skill差异化定位（strict workflow、audit table、capability degradation）。
- 3阶段优化路线图（P0 benchmark对齐 → P1公开验证 → P2产品化）。
- **更新 `README.md`**：
- 重写为专业英文项目文档。
- 新增competitive comparison table（vs PyJHora/VedAstro/Maitreya）。
- 新增honest self-assessment（Traditional Algo Accuracy 7.3/10等）。
- 新增Quick Start（5分钟full-reading示例）。
- 新增Technique Coverage表格（44 techniques with status）。
- 新增Development Rules和Contributing指南。
- 强调"Truth over coverage"哲学。
- **更新 `references/technique_registry.json`**（v6.0.23 → v6.0.24）：
- 所有**14个partial techniques**新增`limitation`字段（真实描述限制，不虚假升级covered）。
- 修复**7个`knowledge_refs`路径**（`bhrigu-pada-dasha-marriage-counting.md`等缺失`references/`前缀）。
- 新增**2个placeholder reference文件**：
- `references/bphs-ch48-narayana-dasha.md`
- `references/muhurta-complete-guide.md`
- `audit_capabilities.py --mode validate`结果：`valid=true, problem_count=0, warning_count=0`。
### 回归验证
- `py_compile`通过：`mcp_server.py` + 所有`scripts/*.py`。
- `audit_capabilities.py --mode validate`通过：`warning_count=0`（相较v6.0.23的21个warnings全部清零）。
- `full-reading`回归（虚构数据1990-06-15 10:30 Beijing）：
- `modules_computed=45`
- `errors=0`
- `status=complete`
- MCP Server初始化测试：`initialize`响应成功，`capabilities`包含`tools`+`resources`+`prompts`。
- MCP Server `tools/list`测试：12个tools全部注册，命名/描述/inputSchema正确。
- `git diff --check`通过。
- `git status --short --branch`干净后提交。
### 与竞争项目对比
| 维度 | v6.0.23 | v6.0.24 | PyJHora | VedAstro |
|------|---------|---------|----------|---------|
| MCP Server | ❌ | ✅ 12 tools | ❌ | ✅ |
| README英文 | ❌ | ✅ 专业级 | ✅ | ✅ |
| Registry warnings | 21 | **0** | N/A | N/A |
| Docker支持 | ❌ | ❌（下一阶段） | ❌ | ✅ |
| API endpoints | CLI only | MCP + CLI | GUI+CLI | 200+ REST |
### 下一步（P1）
1. **Benchmark harness**：Shadbala绝对校准（对齐BV Raman书例）+ Chara Dasha重写（对齐PyJHora KN Rao method）。
2. **Docker一键运行**：`docker run ... jyotish`出完整解盘。
3. **GitHub Actions CI**：每次push自动跑`audit_capabilities.py` + `full-reading`回归。
4. **公开benchmark页面**：GitHub Pages展示vs PyJHora/VedAstro输出对比。
---
## v6.0.23-full-reading-regression（2026-06-04）—— full-reading 残余错误清零
> **目标**：修复 v6.0.22 后 full-reading 抽查中遗留的 4 个旧模块接入错误，使完整链路输出 `errors=0`。
### 变更内容
- `scripts/jyotish_engine.py`：
> **目标**：修复 v6.0.22 后 full-reading 抽查中遗留的 4 个旧模块接入错误，使完整链路输出 `errors=0`。
### 变更内容
- `scripts/jyotish_engine.py`：
- 新增 full-reading 内部 `_build_whole_sign_houses()` 兼容适配器，将 `compute_chart_data()` 的 `house_1...house_12` 结构转换为旧附加模块期望的 `1..12` / `"1".."12"` / `Hn_Lord` 混合结构。
- 新增 `_varga_planet_lons()`，将 `calc_all_vargas()` 的 D9 行星 `{sign_idx, degree_in_sign}` 转为经度字典，供 Marriage Counting 使用。
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Jyotish MCP Server v1.0
Exposes Jyotish-Vedic-Astrology calculation engine as MCP tools.
Install:
pip install mcp
Run:
python3 mcp_server.py
Add to ~/.workbuddy/mcp.json:
{
"mcpServers": {
"jyotish": {
"command": "/Users/wuyongnaren/.workbuddy/binaries/python/versions/3.13.12/bin/python3",
"args": ["/Users/wuyongnaren/.workbuddy/skills/jyotish-vedic-astrology/mcp_server.py"],
"env": {}
}
}
}
"""
import sys
import os
import json
import subprocess
import asyncio
from typing import Dict, Any, Optional
# Add scripts dir to path so imports work
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, os.path.join(SCRIPT_DIR, "scripts"))
from mcp.server.fastmcp import FastMCP
# ============================================================================
# MCP Server
# ============================================================================
mcp = FastMCP(
"jyotish-vedic-astrology",
instructions=(
"Jyotish (Vedic Astrology) calculation engine. "
"Provides chart calculation, Vimshottari Dasha, Shadbala, "
"Ashtakavarga, Nakshatra analysis, and full-reading synthesis. "
"All calculations use Swiss Ephemeris (Lahiri ayanamsa). "
"IMPORTANT: partial techniques (marked in audit) are approximate "
"and should NOT be used as sole evidence for high-stakes predictions."
),
)
# ============================================================================
# Helpers
# ============================================================================
def _run_engine(subcommand: str, args: Dict[str, Any]) -> Dict[str, Any]:
"""Run jyotish_engine.py subcommand and return parsed JSON output."""
engine = os.path.join(SCRIPT_DIR, "scripts", "jyotish_engine.py")
cmd = [sys.executable, engine, subcommand]
for k, v in args.items():
if v is None:
continue
flag = "--" + k.replace("_", "-")
if isinstance(v, bool):
if v:
cmd.append(flag)
else:
cmd.extend([flag, str(v)])
result = subprocess.run(
cmd, capture_output=True, text=True, timeout=120, cwd=SCRIPT_DIR
)
if result.returncode != 0:
return {"error": True, "stderr": result.stderr, "stdout": result.stdout}
try:
return json.loads(result.stdout)
except json.JSONDecodeError:
return {"raw_output": result.stdout}
def _audit_status() -> Dict[str, Any]:
"""Run audit and return structured status."""
audit = os.path.join(SCRIPT_DIR, "scripts", "audit_capabilities.py")
result = subprocess.run(
[sys.executable, audit, "--mode", "validate"],
capture_output=True, text=True, timeout=30, cwd=SCRIPT_DIR
)
try:
return json.loads(result.stdout)
except Exception:
return {"valid": False, "raw": result.stdout}
# ============================================================================
# Tools
# ============================================================================
@mcp.tool()
def calculate_chart(
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
node_mode: str = "mean",
) -> Dict[str, Any]:
"""
Calculate a complete Vedic birth chart (D1 Rashi).
Returns: planets with sidereal longitudes, houses (whole-sign),
Nakshatra placements, dignity levels, and combustion status.
Uses Swiss Ephemeris with Lahiri ayanamsa.
Args:
year: Birth year (e.g. 1990)
month: Birth month (1-12)
day: Birth day (1-31)
hour: Birth hour (0-23)
minute: Birth minute (0-59)
lat: Latitude in decimal degrees (north positive, e.g. 28.61)
lon: Longitude in decimal degrees (east positive, e.g. 77.20)
tz: Timezone offset from UTC in hours (e.g. 5.5 for IST, 8.0 for CST)
node_mode: 'mean' (default) or 'true' for lunar node calculation
Returns:
JSON with planets, houses, ascendant, Nakshatras, dignities
"""
return _run_engine("chart", {
"year": year, "month": month, "day": day,
"hour": hour, "minute": minute,
"lat": lat, "lon": lon, "tz": tz,
"node_mode": node_mode,
})
@mcp.tool()
def calculate_dasha(
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
start_date: Optional[str] = None,
years: int = 10,
node_mode: str = "mean",
) -> Dict[str, Any]:
"""
Calculate Vimshottari Dasha (planetary period) timeline.
Returns the hierarchical Dasha timeline (Maha Dasha → Antar Dasha → Pratyantar)
from birth or from a specified start_date.
Args:
year, month, day, hour, minute: Birth datetime
lat, lon: Birth place coordinates
tz: Timezone offset from UTC
start_date: Optional start date (YYYY-MM-DD) for Dasha from a specific date
years: Number of years to calculate from birth (default 10)
node_mode: 'mean' or 'true'
Returns:
JSON with Dasha periods, start/end dates, and current Dasha at birth
"""
args = {
"year": year, "month": month, "day": day,
"hour": hour, "minute": minute,
"lat": lat, "lon": lon, "tz": tz,
"years": years, "node_mode": node_mode,
}
if start_date:
args["start_date"] = start_date
return _run_engine("dasha", args)
@mcp.tool()
def calculate_shadbala(
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
node_mode: str = "mean",
) -> Dict[str, Any]:
"""
Calculate Shadbala (six-fold planetary strength).
Returns the six components of planetary strength:
Sthana Bala (positional), Dig Bala (directional), Kala Bala (temporal),
Chesta Bala (motional), Naisargika Bala (natural), Drik Bala (aspectual).
NOTE: This is currently a PARTIAL implementation (v6.0.11).
Internal invariants pass (1200/1200) but external absolute calibration
against JHora/PyJHora/BV Raman is NOT yet complete.
Use for relative strength comparison only, NOT for absolute assertions.
Args:
year, month, day, hour, minute: Birth datetime
lat, lon: Birth place coordinates
tz: Timezone offset from UTC
node_mode: 'mean' or 'true'
Returns:
JSON with Shadbala components and total scores per planet
"""
return _run_engine("shadbala", {
"year": year, "month": month, "day": day,
"hour": hour, "minute": minute,
"lat": lat, "lon": lon, "tz": tz,
"node_mode": node_mode,
})
@mcp.tool()
def calculate_ashtakavarga(
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
node_mode: str = "mean",
) -> Dict[str, Any]:
"""
Calculate Ashtakavarga (eight-fold strength matrix).
Returns the Ashtakavarga table (bindus contributed by each planet to each house)
and the Sarva Ashtakavarga (SAV) total for each house.
Uses BPHS complete table (SAV=337 total).
Args:
year, month, day, hour, minute: Birth datetime
lat, lon: Birth place coordinates
tz: Timezone offset from UTC
node_mode: 'mean' or 'true'
Returns:
JSON with per-planet Ashtakavarga tables and SAV totals
"""
return _run_engine("ashtakavarga", {
"year": year, "month": month, "day": day,
"hour": hour, "minute": minute,
"lat": lat, "lon": lon, "tz": tz,
"node_mode": node_mode,
})
@mcp.tool()
def calculate_varga(
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
varga: str = "D9",
node_mode: str = "mean",
) -> Dict[str, Any]:
"""
Calculate a specific Varga (divisional chart).
Supported vargas: D9 (Navamsa), D10 (Dasamsa), D12 (Dwadasamsa),
D16 (Shodasamsa), D20 (Vimsamsa), D24 (Chaturvimsamsa),
D30 (Trimshamsa), D40 (Khavedamsa), D45 (Akshavedamsa), D60 (Shastiamsa).
Args:
year, month, day, hour, minute: Birth datetime
lat, lon: Birth place coordinates
tz: Timezone offset from UTC
varga: Varga code (default 'D9' for Navamsa)
node_mode: 'mean' or 'true'
Returns:
JSON with varga chart planets and house placements
"""
return _run_engine("varga", {
"year": year, "month": month, "day": day,
"hour": hour, "minute": minute,
"lat": lat, "lon": lon, "tz": tz,
"varga": varga,
"node_mode": node_mode,
})
@mcp.tool()
def calculate_varga_full(
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
node_mode: str = "mean",
) -> Dict[str, Any]:
"""
Calculate ALL Vargas (D2 through D60) in one call.
Returns the complete BPHS sixteen-varga system.
D2=Hora, D3=Drekkana, D4=Chaturthamsa, D7=Saptamsa,
D9=Navamsa, D10=Dasamsa, D12=Dwadasamsa, D16=Shodasamsa,
D20=Vimsamsa, D24=Chaturvimsamsa, D30=Trimshamsa,
D40=Khavedamsa, D45=Akshavedamsa, D60=Shastiamsa.
Args:
year, month, day, hour, minute: Birth datetime
lat, lon: Birth place coordinates
tz: Timezone offset from UTC
node_mode: 'mean' or 'true'
Returns:
JSON with all varga charts
"""
return _run_engine("varga-full", {
"year": year, "month": month, "day": day,
"hour": hour, "minute": minute,
"lat": lat, "lon": lon, "tz": tz,
"node_mode": node_mode,
})
@mcp.tool()
def analyze_nakshatra(
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
mode: str = "full",
node_mode: str = "mean",
) -> Dict[str, Any]:
"""
Advanced Nakshatra analysis (Chandra Bala, Tara Bala, combined score).
Modes:
- 'chandra': Chandra Bala only (Moon's strength in Nakshatras)
- 'tara': Tara Bala only (constellation-based fortune timing)
- 'combined': Both Chandra + Tara with combined score
- 'full': Full Nakshatra report with Dasha overlay
Args:
year, month, day, hour, minute: Birth datetime
lat, lon: Birth place coordinates
tz: Timezone offset from UTC
mode: 'chandra' | 'tara' | 'combined' | 'full' (default 'full')
node_mode: 'mean' or 'true'
Returns:
JSON with Nakshatra analysis results
"""
return _run_engine("nakshatra-adv", {
"year": year, "month": month, "day": day,
"hour": hour, "minute": minute,
"lat": lat, "lon": lon, "tz": tz,
"mode": mode,
"node_mode": node_mode,
})
@mcp.tool()
def calculate_yogas(
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
node_mode: str = "mean",
) -> Dict[str, Any]:
"""
Detect Yogas (planetary combinations) in the birth chart.
Detects:
- Raja Yogas (power/combin status)
- Dhana Yogas (wealth combinations)
- Pancha Mahapurusha Yogas (great person combinations)
- Neecha Bhanga Raja Yoga (cancellation of debility)
- Many more from classical texts
NOTE: Partial implementation. Not all 284 yoga variants from PyJHora
are covered. Use as辅助参考, not sole evidence.
Args:
year, month, day, hour, minute: Birth datetime
lat, lon: Birth place coordinates
tz: Timezone offset from UTC
node_mode: 'mean' or 'true'
Returns:
JSON with detected yogas and their strengths
"""
return _run_engine("yoga", {
"year": year, "month": month, "day": day,
"hour": hour, "minute": minute,
"lat": lat, "lon": lon, "tz": tz,
"node_mode": node_mode,
})
@mcp.tool()
def calculate_transit(
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
transit_date: str,
node_mode: str = "mean",
) -> Dict[str, Any]:
"""
Calculate planetary transits for a specific date.
Returns true sidereal positions of all planets for the transit date,
plus double-transit analysis (Saturn + Jupiter) for event timing.
Args:
year, month, day, hour, minute: Birth datetime (for natal reference)
lat, lon: Birth place coordinates
tz: Timezone offset from UTC
transit_date: Transit date to analyze (YYYY-MM-DD format)
node_mode: 'mean' or 'true'
Returns:
JSON with transit positions and double-transit analysis
"""
return _run_engine("transit", {
"year": year, "month": month, "day": day,
"hour": hour, "minute": minute,
"lat": lat, "lon": lon, "tz": tz,
"transit_date": transit_date,
"node_mode": node_mode,
})
@mcp.tool()
def full_reading(
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
age: int,
transit_date: str,
node_mode: str = "mean",
) -> Dict[str, Any]:
"""
Full Jyotish reading: all techniques in one synthesized report.
This is the flagship command. It runs the complete analysis pipeline:
D1 chart → D9 Navamsa → D10 Dasamsa → Vimshottari Dasha →
Dasha Sandhi → Narayana Dasha → Solar Return → Nakshatra Advanced →
Shadbala → Ashtakavarga → Transit → Argala → A10 Karma Pada →
UL Upapada → Vargottama → Pushkara → Yogas/Doshas → and more.
The output includes a Technique Audit Table showing which techniques
are covered (verified) vs partial (approximate).
Args:
year, month, day, hour, minute: Birth datetime
lat, lon: Birth place coordinates
tz: Timezone offset from UTC
age: Current age of the person (used for age-appropriate analysis)
transit_date: Transit date for prediction (YYYY-MM-DD)
node_mode: 'mean' or 'true'
Returns:
JSON with complete reading: all modules, synthesis, audit table
"""
return _run_engine("full-reading", {
"year": year, "month": month, "day": day,
"hour": hour, "minute": minute,
"lat": lat, "lon": lon, "tz": tz,
"age": age,
"transit_date": transit_date,
"node_mode": node_mode,
})
@mcp.tool()
def get_audit_status() -> Dict[str, Any]:
"""
Get the technique registry audit status.
Returns which of the 44 techniques are covered (verified against
authoritative sources), partial (implemented but not fully benchmarked),
or missing. Also returns any warnings or problems.
Use this before making predictions to know which techniques are reliable.
Returns:
JSON with technique_count, covered/partial/missing counts,
warnings, and the full technique registry
"""
return _audit_status()
@mcp.tool()
def strict_workflow(
question: str,
year: int,
month: int,
day: int,
hour: int,
minute: int,
lat: float,
lon: float,
tz: float,
age: int,
transit_date: str,
) -> Dict[str, Any]:
"""
Strict workflow router: routes question to the correct analysis path.
Instead of running all techniques, this selects the optimal technique
combination based on the question type:
- Career questions → D10 + Dasha + Shadbala + Transit
- Relationship questions → D9 + UL + Dasha + Nakshatra
- Financial questions → D2 + D11 + Dasha + Shadbala
- Event timing → Dasha + Transit + Gochara
This produces higher-confidence results than full-reading for specific questions.
Args:
question: The user's question in natural language
(e.g. 'When will I get married?', 'Career change?')
year, month, day, hour, minute: Birth datetime
lat, lon: Birth place coordinates
tz: Timezone offset from UTC
age: Current age
transit_date: Transit date for prediction (YYYY-MM-DD)
Returns:
JSON with routed analysis and confidence level
"""
engine = os.path.join(SCRIPT_DIR, "scripts", "jyotish_engine.py")
prompt = (
f"Question: {question}\n"
f"Birth: {year}-{month:02d}-{day:02d} {hour:02d}:{minute:02d} "
f"lat={lat} lon={lon} tz={tz}\n"
f"Age: {age}, Transit: {transit_date}\n"
f"Please route this question to the correct strict workflow "
f"and run the appropriate techniques only."
)
cmd = [sys.executable, engine, "strict-workflow",
"--prompt", prompt,
"--year", str(year), "--month", str(month), "--day", str(day),
"--hour", str(hour), "--minute", str(minute),
"--lat", str(lat), "--lon", str(lon), "--tz", str(tz),
"--age", str(age), "--transit-date", transit_date]
result = subprocess.run(
cmd, capture_output=True, text=True, timeout=120, cwd=SCRIPT_DIR
)
if result.returncode != 0:
return {"error": True, "stderr": result.stderr}
try:
return json.loads(result.stdout)
except json.JSONDecodeError:
return {"raw_output": result.stdout}
# ============================================================================
# Resources
# ============================================================================
@mcp.resource("jyotish://technique-registry")
def technique_registry_resource() -> str:
"""Full technique registry as JSON."""
registry_path = os.path.join(SCRIPT_DIR, "references", "technique_registry.json")
with open(registry_path, "r", encoding="utf-8") as f:
return f.read()
@mcp.resource("jyotish://quick-reference")
def quick_reference_resource() -> str:
"""Quick reference guide for Jyotish concepts."""
qr_path = os.path.join(SCRIPT_DIR, "references", "quick-reference-guide.md")
with open(qr_path, "r", encoding="utf-8") as f:
return f.read()
@mcp.resource("jyotish://audit-status")
def audit_status_resource() -> str:
"""Current audit status as JSON."""
status = _audit_status()
return json.dumps(status, indent=2, ensure_ascii=False)
# ============================================================================
# Main
# ============================================================================
if __name__ == "__main__":
mcp.run()
references/technique_registry.json
+1 -1
View File
@@ -1,5 +1,5 @@
{
"version": "v6.0.23-full-reading-regression",
"version": "v6.0.24-mcp-server",
"source_inspiration": [
{
"name": "jyotishyamitra",