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vedika-io

@vedika-io/mcp-server

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by vedika-io

vedika_ashtakavarga

Calculate Ashtakavarga scores (0–8 points) to rate planetary strength across 12 signs, returning individual or combined tables for transit timing.

Instructions

Calculate Ashtakavarga — the 8-fold division system scoring each planet strength across 12 signs (0-8 points per sign). "planet" returns Bhinnashtakavarga (individual planet table). "sarva" returns Sarvashtakavarga (combined all 7 planets per sign, max 337 total). Signs scoring 28+ in SAV are strong for transits. Used for timing events. Cost: $0.025/call.

Input Schema

NameRequiredDescriptionDefault
typeYesplanet=individual planet table (requires planet param). sarva=combined all-planets table.
planetNoRequired when type=planet. Which planet to calculate for.
ayanamsaNoSidereal zodiac correction. Default: lahiri. Only for Vedic calculations.
datetimeYesBirth date and time in ISO 8601 format, e.g. "1990-06-15T14:30:00". Required for all chart calculations.
latitudeYesBirth location latitude (-90 to 90). Polar latitudes >66.5 are rejected (house calculations unreliable).
timezoneYesTimezone as UTC offset "+05:30" or IANA "Asia/Kolkata". UTC offsets preferred.
longitudeYesBirth location longitude (-180 to 180).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.9

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden, and it does disclose the paid cost ($0.025/call) plus what each mode returns and the score ranges (0-8 per sign, max 337 SAV). It does not explicitly state that the call is a side-effect-free computation or discuss any auth/permission requirements.

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?

Four dense sentences, front-loaded with the definition and then mode behavior, interpretation threshold, and cost. Every sentence carries usable information with no padding.

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?

For a 7-parameter calculation tool with no output schema, the description covers what both modes return and how to read the scores, which is the essential context. Minor gaps (no mention of the polar-latitude rejection or how the schema handles a missing planet with type=planet) are covered by the schema itself.

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 including the type enum and planet enum is already documented. The description restates the planet vs sarva distinction but adds little beyond what the schema says other than interpretation of the resulting scores.

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?

Specific verb (Calculate) plus a named resource (Ashtakavarga) and immediately defines the system: 8-fold division scoring each planet across 12 signs, 0-8 points. It also distinguishes its two output modes, so an agent can tell it apart from vedika_strength or vedika_dasha.

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?

Gives clear situational context ("Used for timing events") and interprets results ("Signs scoring 28+ in SAV are strong for transits"), which tells the agent when the output matters. It stops short of naming an alternative sibling or stating when not to use it.

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