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HellenicStar Astrology MCP Server

HellenicStar Astrology MCP Server

by qingxiayi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

calculate_lots

Read-onlyIdempotent

Calculates Hellenistic and Arabic lots from birth data and selected lot names, returning longitude, sign, house, formula, and day/night reversal details for chart analysis.

Instructions

Calculate Hellenistic and Arabic lots on demand. Returns name, longitude, sign, house, formula and day/night reversal info. Filter with lots=['fortune', 'spirit', 'eros', 'marriage_male', 'mother', ...]. Data only, no psychological interpretation. 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
lotsNo
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: lots, sect. 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_systemNowhole_sign

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: lots, sect. 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

A4.1/5.0
Behavior5/5

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

Adds substantial information beyond the read-only/idempotent annotations: server-side checks run before results, verification references always identify the complete calculation, and psychological interpretations are explicitly not checked. It also discloses truncation behavior and preserved fields.

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

Conciseness3/5

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

The purpose is front-loaded and the verification instructions are useful, but the middle of the description restates the schema descriptions for detail_level and include almost verbatim, which is wasted space in an already long paragraph.

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 an 11-parameter tool with no output schema, the description explains the return fields and the verification loop well. However, it omits guidance on the date/time and location_id parameters and never mentions the house_system default, so an agent would have to infer whether these inputs are required.

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?

With only 18% schema coverage, the description compensates for lots, include, and detail_level but leaves seven parameters (day, month, year, hour, minute, fold, location_id, house_system) entirely unexplained. The agent cannot tell from the description what these date/time inputs mean for a lots calculation or whether to pass them.

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?

States a specific verb and resource ("Calculate Hellenistic and Arabic lots") and spells out the return fields; the named lots (fortune, spirit, eros, marriage_male) make the astrological technique obvious.

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 explicit usage guidance: filter with lots=[...], omit detail_level and include for the full result, and use summary only for a preview. It also prescribes the verification workflow after the call. It does not compare against sibling calculation tools such as calculate_classical_factors.

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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