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HellenicStar Astrology MCP Server

HellenicStar Astrology MCP Server

by qingxiayi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

calculate_classical_factors

Read-onlyIdempotent

Calculates classical Hellenistic chart factors like sect, dignity, lots, and lunar phase from natal data, enabling structured astrological analysis before interpretation.

Instructions

Hellenistic chart factors from the same natal calculation: sect, planet sect status, essential dignity and condition tags, triplicity lords of the sect light and ascendant, Lot of Fortune and Lot of Spirit, lunar phase, and the Jones whole-chart pattern. Data only, no interpretation text. 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
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: jones_pattern, lots, moon_phase, planet_condition, sect, sect_light, sect_light_triplicity. Use these exact names.
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
house_systemNo

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: jones_pattern, lots, moon_phase, planet_condition, sect, sect_light, sect_light_triplicity. 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

B3.4/5.0
Behavior4/5

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

Beyond the readOnly/idempotent annotations, the description discloses that results are data-only, that server checks run before successful results, and that verify_calculation_claims must be called before interpretation. The detail_level preview/truncation behavior is also explained. It does not describe error handling or input requirements in depth, but adds meaningful behavioral context.

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 front-loaded with the returned factor list and then moves to usage and detail-level behavior. It is somewhat dense but every sentence conveys relevant information, and the verification instruction is clearly stated without excessive repetition.

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 tool with 10 parameters, no output schema, and low schema coverage, the description covers the output contents and verification workflow but leaves invocation semantics incomplete. Missing input details for date/time, location, and house system mean an agent must rely on parameter names alone.

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 20%, so the description must compensate for undocumented parameters. It explains include and detail_level well, but says nothing about the date/time fields, location_id, house_system, or fold, leaving most of the 10 parameters without semantic guidance.

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 clearly enumerates the specific Hellenistic factors returned: sect, dignity, triplicity lords, Lots, lunar phase, and Jones pattern. It establishes the scope as data from the same natal calculation, but does not explicitly differentiate this tool from sibling calculators such as calculate_lots or calculate_natal_chart.

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

Usage Guidelines3/5

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

The description gives a clear verification workflow before interpreting results and explains how detail_level and include affect output. However, it does not state when to choose this tool over the many sibling calculation tools, nor does it list exclusions or prerequisites for the input parameters.

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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