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Calculate evidence-first Dosha patterns

calculate_doshas
Read-onlyIdempotent

Calculates Mangal/Kuja, Kaal Sarpa enclosure, Kemadruma, and conservative Pitru-related structural candidates with raw/effective severity and explicit cancellation or mitigation evidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYes
nameYes
timeYes
placeNoCatalogue query or display label when explicit coordinates are supplied
languageNoen
latitudeNo
timezoneNoRequired IANA timezone when explicit coordinates are supplied, for example Asia/Kolkata
longitudeNo
timezoneOffsetNoOptional fallback offset; derived from timezone and the requested local date when omitted
birthTimeAccuracyMinutesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
safetyYes
subjectYes
summaryYes
patternsYes
rulebookYes
schemaVersionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so safety and repeatability are covered. The description adds that output includes raw/effective severity and explicit cancellation or mitigation evidence, which is useful behavioral context beyond the annotations, but it does not discuss permissions, rate limits, or other operational traits.

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 a single front-loaded sentence that leads with the verb and immediately enumerates the dosha types. It is dense but efficient, with no filler or redundant clauses.

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

Completeness2/5

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

With 10 input parameters, low schema coverage, and no usage guidance, the description is too thin for the tool's complexity. Although an output schema exists (so return values need not be explained), the description omits any explanation of input requirements, location handling, or when this tool is the right choice among many siblings.

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

Parameters1/5

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

Schema description coverage is only 30% across 10 parameters, and the description adds no parameter-level information at all. It does not explain the required name/date/time fields, the anyOf location requirement, the timezoneOffset fallback, or the language enum, leaving the schema's own gaps entirely unaddressed.

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 uses a specific verb ('Calculates') and names four distinct dosha patterns (Mangal/Kuja, Kaal Sarpa enclosure, Kemadruma, Pitru-related candidates), which tells the agent exactly what domain is covered. It does not explicitly differentiate from closely related siblings like analyze_yogas or calculate_strength_profile, so a 4 is appropriate.

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 gives no guidance on when to use this tool versus alternatives such as analyze_yogas or calculate_strength_profile. It only states what is calculated, leaving the agent to infer context from the name and sibling list.

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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