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

Annotations already declare readOnly/idempotent/non-destructive, but the description adds provenance and freshness context the annotations cannot: prices were checked on official pages, carry a checked date, and entries marked "estimate" are unconfirmed. That directly tells an agent how much to trust the returned numbers.

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, front-loaded with the trigger and return shape, with the trust caveat last where it belongs. The service enumeration is slightly long but earns its place by scoping coverage.

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?

There is no output schema, so the description must describe returns, and it does: per-service plans, USD monthly and yearly list prices, and the plan key. Combined with the freshness caveat, an agent has everything needed to call and interpret this tool.

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?

Zero parameters, so the baseline is 4. The description explicitly says "No input," which confirms the agent needs to supply nothing, though there is no further semantic ground to cover.

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 ("List AI subscription plans") and enumerates the covered services plus what each entry contains (plans, USD monthly/yearly list prices, key). This clearly separates it from the sibling aitab_total_ai_subscriptions, which sums rather than lists.

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 an explicit trigger ("when the user asks what AI plans cost"), an explicit sequencing rule ("before aitab_total_ai_subscriptions to look up plan keys"), and an explicit preference against relying on remembered prices. Both when-to-use and the alternative are named.

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?

Annotations already cover the read-only, idempotent, closed-world safety profile, so the description is free to add the parts annotations cannot: the exact return contents (totals, overlaps, shareable receipt link) and, critically, the distinction between 'could_cut' (already included in another plan, safe to drop) and 'consider_dropping' (merely overlaps, ask before recommending a cut). That last point is genuine behavioral guidance for how an agent should act on the output.

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?

Front-loaded with the trigger phrase, then return shape, then prerequisite, then example. Dense but every sentence carries information an agent needs; no filler or restatement of the name.

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?

With no output schema, the description fully carries the burden of describing return values and does so, including the two categorizations of overlap and the receipt link. Combining that with the prerequisites and key syntax, an agent has everything needed to call and interpret this 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 coverage is 100%, so the baseline is 3, but the description adds real meaning not present in the schema: the ':y' suffix convention for yearly billing on individual plan keys, plus a concrete example array. It does not clarify how the ':y' suffix interacts with the separate 'billing' parameter that selects which total the receipt displays first, which is a small ambiguity.

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+resource (total AI subscription spend) with the exact scopes computed: monthly, yearly and daily. It is clearly distinguishable from its sibling aitab_list_ai_plans, which is framed as the source of plan keys rather than the calculator.

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?

Opens with explicit trigger conditions: 'Use when the user lists the AI tools they pay for and asks what they spend per month or year, whether they are paying for overlapping tools, or what a stack of AI subscriptions costs.' It also names the prerequisite path ('Get plan keys from aitab_list_ai_plans'), leaving nothing to inference.

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