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Vedic Astrology and Kundli MCP Server by RoxyAPI

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Find KP sublord changes

post_vedic_astrology_kp_sublord_changes
Read-only

Track when planets cross KP sublord boundaries (1-249 divisions) for precise Krishnamurti Paddhati event timing. Returns exact timestamps when a planet transitions between sublords, essential for prashna kundali analysis and dasha predictions. Use this to find favorable windows when benefic sublords are active. Supports Sun, Moon, Mars, Mercury, Jupiter, Venus, and Saturn tracking over any window up to 365 days.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
planetYesPlanet to track (case-insensitive). Valid values: Sun, Moon, Mars, Mercury, Jupiter, Venus, Saturn
compactNoSet true for the same data in a compact shape: arrays of same-shaped objects arrive columnar as {"__cols":[names],"__rows":[[values]]}. Lossless, typically 40 to 52 percent fewer tokens.
endDateYesLast day of the search, inclusive (YYYY-MM-DD), a calendar day in `timezone`. Not before startDate, at most 365 days after it.
ayanamsaNoAyanamsa system for sidereal conversion. "kp-newcomb" uses the KP-Newcomb dynamic formula, the most common choice for KP astrology. "kp-old" uses the Krishnamurti original table from KP Reader-1 with constant precession rate. "lahiri" uses Lahiri/Chitrapaksha ayanamsa, matching most traditional Vedic software. "raman" uses the B.V. Raman ayanamsa from Hindu Predictive Astrology, a recognised traditional school that sits about 1.45 degrees below Lahiri. Defaults to "kp-newcomb".kp-newcomb
nodeTypeNoLunar node convention. "mean" is the smoothed average node, which always moves retrograde; "true" is the osculating node, which tracks the real perturbed node, oscillates up to about 1.5 degrees either side of the mean on a 173-day cycle, and can briefly turn direct. Neither is more correct and they almost always fall in the same sign. Applies to the Rahu and Ketu positions. Mean is the traditional Vedic default and what printed panchangs use; the choice can move a KP sub-lord in narrow boundary cases, where a span can be as small as 0.5 degrees. Defaults to "mean".mean
timezoneNoIANA name (e.g. "America/New_York", "Europe/London"), a fixed offset like "+05:30", OR decimal hours from UTC. One offset is taken from startDate (DST-correct for that date) and used for the whole range, so a window crossing a daylight-saving change is read on the earlier offset throughout; send a fixed offset if you need that explicit. The two dates are read as calendar days in this timezone and output times are converted to it, so one date with 5.5 is that whole Indian day. Defaults to 0 (UTC).
startDateYesFirst day of the search (YYYY-MM-DD), a calendar day in `timezone`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
planetYes
changesYes
endDateYes
startDateYes
totalChangesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "changes": {
+      "items": {
+        "properties": {
+          "date": {
+            "type": "string"
+          },
+          "datetime": {
+            "type": "string"
+          },
+          "fromKp": {
+            "type": "number"
+          },
+          "fromNakshatraLord": {
+            "type": "string"
+          },
+          "fromSublord": {
+            "type": "string"
+          },
+          "time": {
+            "type": "string"
+          },
+          "toKp": {
+            "type": "number"
+          },
+          "toNakshatraLord": {
+            "type": "string"
+          },
+          "toSublord": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "date",
+          "time",
+          "datetime",
+          "fromKp",
+          "toKp",
+          "fromSublord",
+          "toSublord",
+          "fromNakshatraLord",
+          "toNakshatraLord"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "endDate": {
+      "type": "string"
+    },
+    "planet": {
+      "type": "string"
+    },
+    "startDate": {
+      "type": "string"
+    },
+    "totalChanges": {
+      "type": "number"
+    }
+  },
+  "required": [
+    "planet",
+    "startDate",
+    "endDate",
+    "totalChanges",
+    "changes"
+  ],
+  "type": "object"
+}
  2. Changed4 schema fields changed
    • changedInput schema / properties / endDate / description
      Previous value: -"End date for sublord change search (YYYY-MM-DD format)"New value: +"Last day of the search, inclusive (YYYY-MM-DD), a calendar day in `timezone`. Not before startDate, at most 365 days after it."
    • addedInput schema / properties / planet / enum
      Added value: +[
+  "Sun",
+  "Moon",
+  "Mars",
+  "Mercury",
+  "Jupiter",
+  "Venus",
+  "Saturn"
+]
    • changedInput schema / properties / startDate / description
      Previous value: -"Start date for sublord change search (YYYY-MM-DD format)"New value: +"First day of the search (YYYY-MM-DD), a calendar day in `timezone`."
    • changedInput schema / properties / timezone / description
      Previous value: -"IANA name (e.g. \"America/New_York\", \"Europe/London\") OR decimal hours from UTC. IANA resolved to the DST-correct offset for startDate. Output times are converted to this timezone. Defaults to 0 (UTC)."New value: +"IANA name (e.g. \"America/New_York\", \"Europe/London\"), a fixed offset like \"+05:30\", OR decimal hours from UTC. One offset is taken from startDate (DST-correct for that date) and used for the whole range, so a window crossing a daylight-saving change is read on the earlier offset throughout; send a fixed offset if you need that explicit. The two dates are read as calendar days in this timezone and output times are converted to it, so one date with 5.5 is that whole Indian day. Defaults to 0 (UTC)."
  3. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "endDate": "2025-01-31",
+    "planet": "Moon",
+    "startDate": "2025-01-01"
+  }
+]
    • changedInput schema / properties / compact / description
      Previous value: -"Set true to receive the exact same data in a token-optimized shape that is cheaper for you to read: whitespace is stripped and every array of same-shaped objects is encoded columnar as {\"__cols\":[field names],\"__rows\":[[values]]}, so each field name is sent once instead of once per row. Fully lossless (no field or value is dropped or changed) and typically 40 to 52 percent fewer tokens on large results. Prefer true whenever token or inference cost matters. Default false returns standard indented JSON."New value: +"Set true for the same data in a compact shape: arrays of same-shaped objects arrive columnar as {\"__cols\":[names],\"__rows\":[[values]]}. Lossless, typically 40 to 52 percent fewer tokens."
  4. Changed1 schema field changed
    • changedInput schema / properties / nodeType / description
      Previous value: -"Lunar node type for Rahu and Ketu positions. \"mean\" uses the smooth mean node (traditional Vedic astrology default). \"true\" uses the osculating node with perturbation corrections, oscillating up to 1.5 degrees from mean with a 173-day period. Impacts KP sub-lord assignments in narrow boundary cases. Defaults to \"mean\"."New value: +"Lunar node convention. \"mean\" is the smoothed average node, which always moves retrograde; \"true\" is the osculating node, which tracks the real perturbed node, oscillates up to about 1.5 degrees either side of the mean on a 173-day cycle, and can briefly turn direct. Neither is more correct and they almost always fall in the same sign. Applies to the Rahu and Ketu positions. Mean is the traditional Vedic default and what printed panchangs use; the choice can move a KP sub-lord in narrow boundary cases, where a span can be as small as 0.5 degrees. Defaults to \"mean\"."
  5. Changed2 schema fields changed
    • changedInput schema / properties / ayanamsa / description
      Previous value: -"Ayanamsa system for sidereal conversion. \"kp-newcomb\" uses the KP-Newcomb dynamic formula, the most common choice for KP astrology. \"kp-old\" uses the Krishnamurti original table from KP Reader-1 with constant precession rate. \"lahiri\" uses Lahiri/Chitrapaksha ayanamsa, matching most traditional Vedic software. Defaults to \"kp-newcomb\"."New value: +"Ayanamsa system for sidereal conversion. \"kp-newcomb\" uses the KP-Newcomb dynamic formula, the most common choice for KP astrology. \"kp-old\" uses the Krishnamurti original table from KP Reader-1 with constant precession rate. \"lahiri\" uses Lahiri/Chitrapaksha ayanamsa, matching most traditional Vedic software. \"raman\" uses the B.V. Raman ayanamsa from Hindu Predictive Astrology, a recognised traditional school that sits about 1.45 degrees below Lahiri. Defaults to \"kp-newcomb\"."
    • changedInput schema / properties / ayanamsa / enum
      Previous value: -[
-  "kp-newcomb",
-  "kp-old",
-  "lahiri"
-]New value: +[
+  "kp-newcomb",
+  "kp-old",
+  "lahiri",
+  "raman"
+]
  6. Changed1 schema field changed
    • changedInput schema / properties / compact / description
      Previous value: -"Return the same data in a token-optimized compact shape (minified, with same-shaped arrays encoded columnar) to reduce LLM token cost. Lossless: no fields are dropped. Default false."New value: +"Set true to receive the exact same data in a token-optimized shape that is cheaper for you to read: whitespace is stripped and every array of same-shaped objects is encoded columnar as {\"__cols\":[field names],\"__rows\":[[values]]}, so each field name is sent once instead of once per row. Fully lossless (no field or value is dropped or changed) and typically 40 to 52 percent fewer tokens on large results. Prefer true whenever token or inference cost matters. Default false returns standard indented JSON."
  7. Changed1 schema field changed
    • addedInput schema / properties / compact
      Added value: +{
+  "default": false,
+  "description": "Return the same data in a token-optimized compact shape (minified, with same-shaped arrays encoded columnar) to reduce LLM token cost. Lossless: no fields are dropped. Default false.",
+  "type": "boolean"
+}
  8. Changed2 schema fields changed
    • changedInput schema / properties / timezone / anyOf
      Previous value: -[
-  {
-    "maximum": 14,
-    "minimum": -14,
-    "type": "number"
-  },
-  {
-    "pattern": "^[A-Za-z_]+(?:\\/[A-Za-z0-9_+-]+){0,2}$",
-    "type": "string"
-  }
-]New value: +[
+  {
+    "maximum": 14,
+    "minimum": -14,
+    "type": "number"
+  },
+  {
+    "type": "string"
+  }
+]
    • changedInput schema / properties / timezone / description
      Previous value: -"Decimal hours from UTC OR IANA name (e.g. \"Asia/Kolkata\"). IANA resolved to the DST-correct offset for startDate. Output times are converted to this timezone. Defaults to 0 (UTC)."New value: +"IANA name (e.g. \"America/New_York\", \"Europe/London\") OR decimal hours from UTC. IANA resolved to the DST-correct offset for startDate. Output times are converted to this timezone. Defaults to 0 (UTC)."
  9. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds behavioral detail beyond annotations: it returns exact timestamps, supports seven specific planets, and enforces a 365-day window. No contradictions with annotations.

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?

The description is three sentences, front-loaded with the core purpose, then elaborates on use case and supported scope. Every sentence earns its place with no redundancy or fluff.

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?

An output schema exists, so return format is already specified elsewhere. The description covers the essential purpose, usage context, and constraints (planet set, window limit), making it complete for an agent to decide whether to call this tool and how.

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?

Schema description coverage is 100%, so every parameter (planet, startDate, endDate, compact, ayanamsa, nodeType, timezone) is already documented with detailed descriptions. The tool description mentions the planet list and window limit but adds no meaning beyond the schema, meeting the baseline for high 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 tracks when planets cross KP sublord boundaries (1-249 divisions) and returns exact timestamps, clearly distinguishing it from related tools like rasi changes. It also names the supported planets and the 365-day window, leaving no ambiguity about what the tool does.

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?

The description gives a concrete use case ('find favorable windows when benefic sublords are active') and mentions it's essential for prashna kundali analysis and dasha predictions. It does not explicitly contrast with sibling tools (e.g., kp_rasi_changes) or state when not to use it, but the purpose is clear enough to guide selection.

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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