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

Annotations already confirm readOnlyHint=true and destructiveHint=false, so the tool is semantically read-only. The description adds behavioral context: the ayanamsa is computed dynamically per instant, not from a table; it explains precision (changes ~0.01°/month, ~0.14″/day) and that output is ready for subtraction from tropical longitudes. No contradictions. The description adds a clear behavioral layer beyond the minimal annotation.

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 paragraph of about 100 words, front-loaded with the core computation purpose, then explains param usage, then situates in the KP workflow. Near the end, it includes a redundant keyword list that is not needed for AI understanding and adds noise. Removing that tail would make it a 5. Still, the essential structure is efficient and informative.

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?

Given the tool has no output schema, 4 params, and is a simple get-a-value operation, the description is complete. It tells what is returned (precession offset), how it relates to other KP tools (internal standard), and how accuracy varies by param presence (date only vs. date+time+tz). For its complexity level, nothing essential is missing.

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 coverage is 100% (all 4 parameters have descriptions), so baseline is 3. The description adds further semantic value: it explains the relation to 'midnight UTC' default, when time matters (arcsecond-level precision), and provides natural language context for why timezone is needed (IANA vs. offset handling, DST awareness). This elevates it above the baseline because it enriches understanding beyond the schema examples.

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 computes the specific 'KP-Newcomb (Krishnamurti) ayanamsa' for any instant, using continuous precession theory. It distinguishes itself from table-lookup methods and among siblings (e.g., many post tools for chart calculations vs. this simple get for ayanamsa). The description explicitly calls out the practical purpose: obtaining the precession offset for KP charts.

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 explains when to override defaults (e.g., 'to pin a birth moment exactly'), offers clear alternatives for date-only vs. date+time+timezone, and connects to sibling tools by stating the value is 'what makes a KP chart reproduce the reference software' and is 'the same value every KP endpoint applies internally.' It lacks explicit when-not-to-use guidance (e.g., if another ayanamsa system is needed), but for the intended KP use case it is clear.

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