Jyotish — Vedic astrology for AI agents

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

With no annotations, the description carries the burden. It discloses the accuracy-gated path, including chart-anchor check and rewrite retry, and mentions the response envelope includes citation_status and accuracy-gate notes. This gives meaningful behavioral context beyond the schema, though it doesn't cover failure modes or performance characteristics.

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

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

The description is a single, compact paragraph that leads with the core purpose, then adds accuracy details, response envelope notes, and lang. Every sentence adds value without redundancy, though it could be slightly better structured with bullets for readability.

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

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

The tool has a nested birth_data object and no output schema. The description explains the response envelope concept but does not detail the structure or potential error conditions. While required parameters are in the schema, the absence of an output schema makes the description incomplete for an agent expecting a precise return format.

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

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

Schema description coverage is only 33% (only 'question' has a description among top-level params). The description adds nothing about 'question' or 'birth_data' semantics; it only mentions 'lang' which is already enumerated in the schema. With low coverage, the description fails to compensate, leaving parameter meaning underspecified.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool answers a question via grounded synthesis from the birth chart, and distinguishes it from siblings by emphasizing 'synthesis' rather than raw facts (get_chart_facts), panchang (get_panchang), or compatibility (guna_milan). The verb 'answer' and resource 'birth chart' are explicit.

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

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

The description implies usage for synthesis questions but never explicitly contrasts with sibling tools or states when not to use it. No alternative tool is named, leaving the agent to infer based on the phrase 'grounded synthesis answer'. This is adequate but not explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the disclosure burden. It clearly states determinism, pure computation, no LLM involvement, and classical-text citations for yogas, which signals reproducibility and trustworthiness. It does not discuss edge cases or error behavior, but for a pure read-only computation the key behavioral traits are disclosed.

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

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

The description is two sentences with no filler. The first sentence front-loads scope and enumerates the output types; the second adds the crucial no-LLM determinism guarantee and endpoint equivalence. Every clause earns its place.

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

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

The tool is complex and has no output schema, but the description compensates by listing the major returned categories: lagna, nakshatra, dasha, house placements, dignities, combustions, D9/D10 ascendants, Sade Sati, yogas with citations, and dasha timeline. It lacks mention of ayanamsa/house-system defaults or error scenarios, but an agent has enough context to invoke and interpret the result correctly.

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

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

Schema description coverage is 100%, so all five parameters are already documented with formats and ranges. The description only groups them under 'birth data' and does not add semantic detail beyond the schema, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource: 'Deterministic Vedic chart facts for birth data' followed by a concrete enumeration of outputs such as lagna, dasha, house lords, and Sade Sati standing. It also distinguishes itself from likely LLM-based siblings with 'Pure computation, no LLM.'

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

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

The description implies clear usage: use when deterministic, reproducible chart facts are needed rather than interpretive LLM answers. 'Pure computation, no LLM' provides an implicit when-not, and 'Same output as POST /api/b2b/chart-facts' gives an integration reference, though it does not explicitly name sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden. It discloses that the tool is deterministic, uses no LLM, and follows the same compute path as a specific API endpoint, which is valuable behavioral context. It does not mention error cases, rate limits, or output format, but the deterministic and API-parity statements give an agent a solid behavioral model.

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

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

Two sentences with no wasted words. The core purpose and return values are front-loaded, and the deterministic/no-LLM differentiator is placed at the end. Every sentence earns its place.

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

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

For a read-only computation tool with 100% schema coverage and no output schema, the description is nearly complete. It covers what is returned, the default date behavior, and the deterministic nature. It could mention that the output is a structured object or list, but the enumerated components already give an agent a good sense of the return. The lack of annotations is partially offset by the description's clarity.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters. The description adds the default behavior for 'date' (local today at the location) and clarifies that lat/lng are location coordinates, but it does not add meaning beyond the schema for tz. Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('get') and resource ('panchang'), enumerates the exact components returned (tithi, paksha, nakshatra, yoga, karana, vara, sunrise/sunset), and clarifies the optional date with a default. It distinguishes itself from siblings by being a deterministic, no-LLM computation, which is a clear differentiator from ask_jyotish.

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

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

The description implies when to use it: when a deterministic panchang computation is needed, and explicitly contrasts with 'no LLM' and 'same compute path as GET /api/panchang', which helps an agent choose it over ask_jyotish. It does not explicitly name alternatives or state when not to use it, but the deterministic/no-LLM framing provides clear context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the behavioral burden and does so well: it discloses that the computation is deterministic and LLM-free, that Moon nakshatra/rashi are derived through the same chart engine as get_chart_facts, and that the output is auditable with per-koota scores, maximums, and reasons. It could add more detail about result shape or ordering, but the key behaviors are covered.

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

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

Four sentences with no filler: purpose and field list are front-loaded, the computation rule is stated directly, the deterministic/no-LLM note is a single fragment, and the output/auditability promise earns its place.

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

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

The input schema is exhaustive and the description covers the output shape at the per-koota level (score, maximum, plain-language reason), which is important because there is no output schema. A small gap is that the top-level response shape and potential order sensitivity between person_a and person_b are not addressed, but the essential calling information is present.

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

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

The schema already provides 100% coverage of dob/tob/lat/lng/tz for person_a and person_b, so the baseline is 3. The description adds value beyond the schema by clarifying that these parameters are raw birth data and that nakshatra/rashi must not be passed, which prevents a common misuse.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific domain and resource: Vedic compatibility (Guna Milan/Ashtakoota) between two people, and quantifies it as 36 points across eight named kootas. This clearly identifies the tool's purpose and distinguishes it from sibling tools like get_chart_facts (single chart), get_panchang (calendar), and ask_jyotish (query).

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

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

It conveys the intended use case — pairwise compatibility via birth charts — and gives an explicit rule: pass raw birth data and do not pass nakshatra/rashi because they are computed internally. It does not explicitly contrast against sibling tools or state when not to use it, but the context is clear enough for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.