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Vedaksha

by arthiqlabs
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Rust
Hybrid

compute_synastry

Read-only

Compare two sidereal charts to find every graha-to-graha aspect, with type, orb, and strength. Uses only longitudes, so synastry works without birth times or places.

Instructions

Compute synastry — the aspects each graha in one chart makes to each graha in another chart. Every graha in chart A is tested against every graha in chart B (the two charts do not need the same graha names), and each hit is returned with its aspect type, orb in degrees and strength (1.0 at exact, falling linearly to 0.0 at the orb boundary). Orbs are the traditional Lilly values — conjunction, trine and opposition 8°, square 7°, sextile 6°, minors 2° — scaled by orb_factor. Longitudes only: this tool needs no birth time, place or ephemeris.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chart_aYesMap of graha name to sidereal longitude [0, 360) for the first chart, e.g. {"Sun": 10.0, "Moon": 130.0}.
chart_bYesMap of graha name to sidereal longitude [0, 360) for the second chart. Need not use the same names as chart_a.
aspect_setNo'major' (default) tests the five Ptolemaic aspects; 'all' adds semi-sextile, semi-square, quintile, sesquiquadrate, bi-quintile and quincunx.major
orb_factorNoMultiplier on the default orbs. 1.0 (default) is standard, 0.5 is tight. Must be greater than 0 and at most 5.0, above which the major-aspect windows cover every separation.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
aspectsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv8.0.0
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "aspects": {
+      "items": {
+        "properties": {
+          "aspect_type": {
+            "description": "Aspect name.",
+            "type": "string"
+          },
+          "chart_a_planet": {
+            "description": "Graha from the first chart.",
+            "type": "string"
+          },
+          "chart_b_planet": {
+            "description": "Graha from the second chart.",
+            "type": "string"
+          },
+          "orb": {
+            "description": "Departure from exact, in degrees.",
+            "type": "number"
+          },
+          "strength": {
+            "description": "Normalised strength in [0, 1], 1 at exact.",
+            "type": "number"
+          }
+        },
+        "required": [
+          "chart_a_planet",
+          "chart_b_planet",
+          "aspect_type",
+          "orb",
+          "strength"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "aspects"
+  ],
+  "type": "object"
+}
  2. First observedv7.0.0

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses detailed behavior beyond the readOnlyHint annotation: every graha in chart A is tested against every graha in chart B, hits include aspect type/orb/strength, strength falls linearly from 1.0 to 0.0, and orbs follow Lilly values scaled by orb_factor. This gives the agent a precise model of what the tool will do.

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?

Three dense sentences: the first states the core purpose, the second details the algorithm and output, and the third states the input prerequisites. Every sentence carries necessary information without repetition or filler.

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?

For a tool with four parameters, two of which are nested objects, the description covers the algorithm, the output shape, orb conventions, scaling behavior, and the minimal input requirements. The presence of an output schema further reduces the need to document return values, so nothing essential is missing.

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?

The input schema already provides 100% parameter coverage, so the baseline is 3. The description adds meaningful semantic detail beyond the schema by explaining the orb values, orb_factor scaling, and strength formula, which clarifies how orb_factor changes results.

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 states a specific verb and resource: compute synastry aspects between two charts, with the precise cross-testing model. It clearly distinguishes this from siblings like compute_composite or compute_drishti by describing exactly what the tool computes.

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 clear context for when this tool is appropriate: comparing two charts by longitude only, with no need for birth time, place, or ephemeris. It does not explicitly name alternatives or exclusion conditions, but the scope is unambiguous.

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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