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Asterwise

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Lo Shu Grid

asterwise_get_lo_shu_grid
Read-onlyIdempotent

Generate a Lo Shu grid from a birth date to reveal missing, repeated, and per-digit traits with plane annotations. Get a clear numerological reading for any date in YYYY-MM-DD format.

Instructions

Derives a Lo Shu three-by-three frequency grid from birth-date digits and annotates planes, missing or repeated digits, and per-digit traits.

WORKFLOW: BEFORE: None — standalone. AFTER: None.

INPUT CONTRACT: date string only; validated upstream.

DO NOT CONFUSE WITH: asterwise_get_numerology_profile — letter-based Western numbers, not digit-frequency Lo Shu. asterwise_get_name_correction — spelling harmonics, not birth-date grids.

Full output and error contract: https://docs.asterwise.com/mcp/tools/get-lo-shu-grid/

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesDate in YYYY-MM-DD format. Defaults to today when omitted.
response_formatNoOutput format: 'markdown' (default) for a readable report, or 'json' for the raw structured payload.markdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

The annotations already declare readOnly, idempotent, non-destructive, and openWorld=false. The description adds that it is standalone and that input is validated upstream, providing a small amount of extra context about side effects and error handling. However, the description does not elaborate on output behavior or error responses, so the added transparency is modest.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (description, workflow, input contract, do-not-confuse, link). The main functionality is stated in one concise sentence, and the additional sections are brief and purposeful. It is slightly longer due to the exclusions, but remains efficient overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description mentions the key outputs (planes, missing/repeated digits, per-digit traits) and provides a link to the full output and error contract. However, the output schema is not included in the prompt, and the description does not explain the output structure beyond those annotations, nor does it elaborate on error cases. The link helps but is not directly accessible.

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?

The schema states 'date' is required, but the description says 'Defaults to today when omitted' – a direct contradiction. Additionally, the 'INPUT CONTRACT' line says 'date string only' while the schema includes a second parameter, response_format. This inconsistency and omission undermine the clarity of the two parameters, despite the schema descriptions being otherwise clear.

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 clearly states the tool's function: deriving a Lo Shu three-by-three frequency grid from birth-date digits and annotating planes, missing/repeated digits, and per-digit traits. It also explicitly distinguishes itself from two similar siblings (numerology profile and name correction) by contrasting letter-based vs digit-based methods, making its purpose unmistakable.

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 'DO NOT CONFUSE WITH' section gives explicit when-not-to-use guidance for two closely related tools (numerology profile and name correction), and the workflow section notes it is standalone. This helps an agent select the correct tool among many siblings, though it does not exhaustively list all alternative cases.

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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