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Dinesh-Gahlot

mcp-indian-astrology

divine_get_bhava_group_predictions

Read-onlyIdempotent

Generate birth chart predictions organized by classical house groups: kendra, trikone, trishadaya, trik, maraka, and dhana.

Instructions

Get predictions grouped by house category for a birth chart.

Returns interpretations for the classical house groups: kendra (angular), trikone (trinal), trishadaya, trik (dusthana), maraka and dhana.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.9.1

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that the tool returns 'interpretations' rather than raw positions or numbers)Skip a useful behavioral detail about response content, but it does not mention response size, pagination, or any special processing behavior beyond the annotation-backed guarantees.

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?

Two sentences with no filler: the first gives the immediate purpose, the second tells exactly which house groups are covered. Every phrase earns its place, and the most important information (grouping by house category) is front-loaded.

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 read-only tool with an output schema, the description is quite complete: it names the domain (birth chart), states the output shape (grouped interpretations), and lists the included categories. It does not explicitly mention that precise birth time and place are required, but the schema's required fields and the KundliInput definition compensate.

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

Parameters2/5

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

Schema description coverage is 0% for the single top-level 'params' parameter, and the tool description provides zero information about input requirements. The nested KundliInput definition does describe individual birth fields, and the tool name implies a birth chart, but the description neither explains that full birth details are needed nor gives any parameter-specific context, leaving an important gap for the agent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Get predictions grouped by house category for a birth chart') and enumerates the exact classical house groups returned (kendra, trikone, trishadaya, trik, maraka, dhana). It clearly identifies what the tool does, though it does not explicitly contrast itself with sibling tools like divine_get_bhava_kundli or divine_get_bhava_analysis.

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

Usage Guidelines2/5

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

The description offers no guidance on when to use this tool versus the many related bhava, dasha, and transit tools in the sibling list. It states what predictions are returned but never provides conditions, exclusions, or alternative tool suggestions, leaving the choice to inference.

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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