Kaalshodh — Indian astronomy time machine

find_copresence

Read-only

Find every window when the given planets occupy the same rashi (sign)
simultaneously — conjunction-class questions over ±5000 years, instant.

Example questions: "when are Guru and Shani next together in Kumbha?"
(the ~60-year social-cycle conjunction), "when were Guru, Shani and
Mangala last in one sign?", "were all planets really in one rashi at the
Kali Yuga epoch (Feb 3102 BCE)?", "great conjunctions in the 1st
millennium".

Resolution is the rashi (30°) — for exact-degree conjunctions treat these
windows as candidates. Including Chandra gives many short (~2 day)
windows; omit it for long-period questions.

Args:
    grahas:     Two or more of: Surya, Chandra, Mangala, Budha, Guru,
                Shukra, Shani, Rahu, Ketu.
    start_date, end_date: YYYY-MM-DD, negative years allowed.
    rashi:      Optional sign filter — Vedic (Mesh..Meen) or western
                (Aries..Pisces) name, or 1..12. Omit = any shared sign.
    ayanamsa:   "Lahiri" (default) or "Sayan".
    max_results: Cap (≤100); response flags truncation.

Input Schema

TableJSON Schema

Name	Required	Default
`rashi`	No
`grahas`	Yes
`ayanamsa`	No	Lahiri
`end_date`	Yes
`start_date`	Yes
`max_results`	No

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses important behavioral traits: 'instant' performance, 'Resolution is the rashi (30°)' (indicating approximate results), the effect of including Chandra ('many short (~2 day) windows'), and the max_results cap with truncation flags. This adds meaningful context beyond the annotation.

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

Conciseness5/5

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

The description is front-loaded with the core purpose, followed by illustrative examples, a key precision caveat, and a parameter list. Every sentence adds value—there is no fluff. The structure is logical and easy to scan, making it both concise and informative.

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

Completeness5/5

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

Given the tool's complexity (6 parameters, no output schema), the description covers all essential aspects: parameter semantics, precision limits, behavioral quirks (Chandra windows), and response truncation. It even flags output behavior via 'response flags truncation,' making it near-complete for an agent to invoke and interpret results.

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

Parameters5/5

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

Although the schema has 0% description coverage, the tool description's Args section explains each parameter thoroughly: it lists valid grahas, date formats with negative years, the optional rashi filter with naming conventions, the ayanamsa default ('Lahiri') and alternative ('Sayan'), and the max_results cap. This fully compensates for the schema's lack of descriptions.

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 with a specific verb and resource: 'Find every window when the given planets occupy the same rashi (sign) simultaneously.' It also provides example questions that illustrate the scope, distinguishing it from siblings like get_planetary_positions (which likely returns positions, not conjunction windows).

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 description gives strong contextual guidance through example questions and notes like 'conjunction-class questions over ±5000 years' and 'omit it for long-period questions.' It doesn't explicitly name alternative tools or say when not to use it, but the context is clear enough for an agent to infer appropriate usage.

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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TDQS

A4.7/5.0

Disambiguation5/5

Each tool targets a distinct query type: planetary conjunctions, panchang date matching, event lists, nearest events, and position snapshots. Even the similar-looking get_astro_events and get_nearest_event are clearly differentiated by range vs. nearest-neighbor scope.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: find_copresence, find_matching_dates, get_astro_events, get_nearest_event, get_planetary_positions. 'find_' for search operations and 'get_' for retrieval operations, with snake_case throughout.

Tool Count5/5

Five tools is well within the ideal 3-15 range for a domain-specific server. Each tool covers a meaningful, non-redundant capability for astronomical and panchang time travel, and there is no bloat or triviality.

Completeness4/5

The set covers major workflows: positions, events, conjunctions, and panchang-based date search. The only notable gap is a direct 'panchang for a specific date' lookup, which find_matching_dates cannot handle without constraints and which is explicitly delegated to another MCP server. This is a minor workaround rather than a dead end.

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