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HellenicStar Astrology MCP Server

HellenicStar Astrology MCP Server

by qingxiayi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

calculate_saturn_jupiter_cycles

Read-onlyIdempotent

Calculates Saturn-Jupiter cycle events over natal angles, including exact conjunctions with ASC/IC/DSC/MC, aspects, quadrant phases, and current cycle positions as of a chosen date.

Instructions

Saturn and Jupiter cycles over the natal angles: exact conjunctions with ASC, IC, DSC and MC, Saturn-Jupiter aspects, the quadrant phases of each planet, and the current position in each cycle on as_of (default today). max_age 1-120, default 80. Server checks run before successful results are returned. Before interpreting this data, call verify_calculation_claims with its unchanged calculation_reference and draft source-field claims. Correct contradicted facts and reverify before the final answer. Psychological interpretations are not checked. Optional detail_level=summary previews object/nested rows (up to five), preserves complete scalar membership lists, and marks truncation; include selects top-level content fields. Omit both for the original full result. Verification references always identify the complete checked calculation.

Input Schema

NameRequiredDescriptionDefault
dayNo
foldNo
hourNo
yearNo
as_ofNo
monthNo
minuteNo
includeNoOptional 1-30 top-level content field names, each 1-80 characters. Omit to return all content. Control/provenance fields remain; unknown or control field names return invalid_input with available_fields. Known selectable fields: as_of, aspects, current, jupiter_events, jupiter_phases, natal, range, saturn_events, saturn_phases, stats. Use these exact names.
max_ageNo
location_idNo
detail_levelNofull returns complete calculation content. summary previews up to five object/nested rows per list, preserves scalar membership lists, and reports omitted counts. References still identify the full calculation.full

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.7
    • changedInput schema / properties / include / description
      Previous value: -"Optional 1-30 top-level content field names, each 1-80 characters. Omit to return all content. Control/provenance fields remain; unknown or control field names return invalid_input with available_fields."New value: +"Optional 1-30 top-level content field names, each 1-80 characters. Omit to return all content. Control/provenance fields remain; unknown or control field names return invalid_input with available_fields. Known selectable fields: as_of, aspects, current, jupiter_events, jupiter_phases, natal, range, saturn_events, saturn_phases, stats. Use these exact names."
  2. Changed2 schema fields changed
    • addedInput schema / properties / detail_level / description
      Added value: +"full returns complete calculation content. summary previews up to five object/nested rows per list, preserves scalar membership lists, and reports omitted counts. References still identify the full calculation."
    • addedInput schema / properties / include / description
      Added value: +"Optional 1-30 top-level content field names, each 1-80 characters. Omit to return all content. Control/provenance fields remain; unknown or control field names return invalid_input with available_fields."
  3. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior, and the description adds useful context: server checks run before successful results, verification references identify the full calculation, and detail_level/include affect truncation and field selection. It does not explain error modes in depth, but the key behavioral workflow is disclosed.

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 purpose is front-loaded and most sentences carry useful operational information. The description is dense and could be more scannable, and it repeats some include/detail_level semantics already present in the schema, but it is not padded with irrelevant content.

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?

For a complex tool with no output schema, the description usefully names output content fields, explains detail_level and include, and describes the verification workflow. However, the incomplete coverage of the birth date/time/location parameters leaves an important part of the invocation context underspecified.

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 only 18%, so the description must compensate. It explains as_of, max_age, detail_level, and include, but leaves day, month, year, hour, minute, fold, and location_id undocumented, which is a significant gap for an 11-parameter natal-calculation tool.

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: calculating Saturn and Jupiter cycles over natal angles, including conjunctions, aspects, quadrant phases, and current cycle position. It clearly distinguishes this from generic chart rendering and from the verification tool it references.

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?

It gives a clear usage context and an explicit post-calculation workflow: call verify_calculation_claims before interpreting the data, correct contradicted facts, and reverify. It also notes that psychological interpretations are not checked. It does not explicitly contrast this with render_saturn_jupiter_cycles or other calculation siblings, but the context is strong.

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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