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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds real behavioral value: the exact weighting formula applied and the guardrail that missing marks must be requested rather than guessed. Output schema handles return values, so no gap there.

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?

Three sentences, zero waste, front-loaded with the trigger condition followed by the formula and the exclusion. 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 deterministic calculation tool with full schema coverage and an output schema, the description supplies everything an agent needs: trigger, formula, missing-data behavior, and scope boundary.

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 every parameter including defaults and the Hafiz/NCC bonus semantics. The description only restates the formula weights; it adds little parameter-level meaning beyond what the schema provides, so the baseline 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?

States a specific verb and resource (calculate the UET Taxila merit aggregate), names the exact inputs it consumes (SSC, HSSC Part-I, entry test), and explicitly scopes out other universities, so it is distinguishable from the sibling UET lookup/estimate tools.

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?

Gives explicit triggering conditions ('when a user gives their SSC, HSSC Part-I and entry test marks and wants merit, eligibility, or program chances'), an explicit exclusion ('Do not use it for other universities' formulas'), and a behavioral instruction for missing data ('Ask for any missing marks; never guess them').

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