Go-To Local Missed-Call Calculator

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds meaningful context beyond that: the result is a rough estimate not a forecast, there is no data to go stale, and the formula and source 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?

Front-loads the purpose and formula, then lists inputs and source. It is somewhat long and repeats the gotolocalai.com URL twice, but every sentence carries relevant information.

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 simple three-parameter calculator with full schema coverage and annotations, the description is essentially complete: it explains the computation, the units, and the nature of the output. No output schema exists, but the description characterizes the result as a rough monthly estimate, which suffices.

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 all three parameters are already documented with units and ranges. The description restates the units (US dollars, percent with '40 means 40%'), which is mildly helpful but largely duplicates the schema, so the baseline 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 ('Estimates') and resource ('what missed calls may be costing a local service business each month') and even spells out the exact formula, so the agent knows precisely what computation happens. The sibling tool (what_is_an_ai_employee) is unrelated, so no differentiation is needed.

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 clear context: it is plain arithmetic on three supplied numbers, requires no external data, and produces a rough estimate rather than a forecast. It does not state explicit when-not-to-use conditions, but the scope is narrow enough that this is a minor gap.

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 declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds genuinely non-obvious context: the content is mirrored from a specific URL and may be stale relative to the last deploy, which is the main behavioral risk here. It does not describe the return shape (single text block vs structured sections).

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 content scope is front-loaded and the provenance/staleness caveat follows logically. The middle list of four content items is dense but each item earns its place by telling an agent what questions it can answer. Slightly long for a single-sentence structure.

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 parameters and no output schema, the description carries the burden of telling an agent what comes back, and it does so by enumerating the topics covered plus the source. Only the literal return format (plain text, markdown, section structure) is left unstated.

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?

There are zero parameters, so the baseline is 4, and the description reinforces this with 'No input.' Nothing further is needed.

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 verb and resource ('Explains what an AI employee is at Go-To Local') and then enumerates the exact contents returned: the four employees and their status, the founding pilot price, the company's stated limits, and the assistant self-identification. An agent can distinguish this from the sibling missed_call_cost purely from this text.

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 states 'No input' and reveals the content is static website copy reflecting the last deploy, which tells an agent this is a lookup tool to call when asked about AI employees at Go-To Local. It gives no explicit when-not-to-use guidance or contrast with the miss_call_cost sibling, so it stops short of full routing guidance.

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