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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, but the description goes well beyond them: it discloses the computation formula, the Gazetted vs Restricted distinction, substitute-day and selection policies surfaced in the output, and even the direct REST fallback endpoint. This is rich behavioral context on top of a complete safety profile.

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?

Well front-loaded with usage routing first, then computation, inputs, examples, outputs, formula and fallback. It is verbose for a two-parameter tool and the inline JSON argument examples partially duplicate the schema's examples array, but every section is clearly labeled and earns its keep.

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 single-date calculation tool with an output schema present, the description supplies everything needed: scope constraints, the computation rule, policy fields it returns, dataset/version context, and a REST fallback plus docs link. Nothing required to invoke it correctly 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 description coverage is 100% and both parameters (date, office_scope) are documented there, so the baseline is 3. The description adds value with worked user-story examples and full JSON argument examples that demonstrate the two enum values in realistic use, though the raw syntax itself is already in the schema.

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 and resource ('Checks a 2026 date against the Department of Personnel and Training central-government holiday schedule') and pins the calendar scope precisely, which is exactly what separates it from the country-specific check_* siblings. It also clarifies the deterministic single-date semantics versus the add_/count_ india variants by telling the agent to 'Call this tool directly and return its calculation.'

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?

Explicit when-to-use ('deterministic 2026 central-government office baseline ... Delhi/New Delhi versus outside Delhi') and when-not-to-use (state, UT, bank, employer, school, exchange, or outside-Delhi locally selected optional-holiday calendars). It further instructs the agent not to answer from memory or stop at discovery, which is a rare and valuable routing instruction.

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