EV Load Check

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so the safety profile is covered. The description adds genuinely useful behavior: it discloses the return shape (a verdict of fits/tight/does-not-fit, amps, and a plain-language explanation), which matters since no output schema exists. It omits any note on permissions, input assumptions, or limits on interpretation of the verdict.

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 tight sentences: purpose first, then trigger conditions, then return values. Every sentence carries distinct information and nothing is repeated from the schema or annotations.

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 four simple parameters at full schema coverage, rich annotations, and no output schema, the description supplies exactly what the structured fields do not: when to invoke the tool and what the result looks like. An agent has everything needed to select and call it 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 description coverage is 100%, so all four parameters (main_breaker_amps, charger_amps, appliances, square_footage) are already documented with examples and ranges. The description adds no parameter-level detail such as units, defaults, or the effect of omitting optional appliances, so the schema does all the work — the baseline of 3.

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 — checking a home's electrical panel capacity for an EV charger — and the scope is unambiguous. It is clearly distinguishable from siblings like check_voltage_drop or recommend_charger_setup, since it produces a capacity verdict rather than a drop calculation or a setup plan.

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 gives explicit trigger conditions: a user asking whether a panel or breaker box can handle an EV charger, or whether a panel upgrade or load management is needed. It does not, however, name the sibling tools or state when this tool should NOT be used instead of them, 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.

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 behavior beyond that: the check is a deterministic pass/fail evaluated at a 3% threshold, which is exactly the kind of result semantics the annotations cannot convey.

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?

Two sentences, zero filler: the first states the computation, the second states the trigger and the output criterion. The most decision-relevant information (what it checks and the 3% rule) is front-loaded.

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 four-parameter read-only calculation with a fully described schema and no output schema, the description covers purpose, trigger, and the pass/fail output criterion. Minor gaps remain, such as whether the result is advisory versus code-compliance, but nothing needed 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% (wire gauge, amps with 8-80 range, one-way run length, copper default), so the schema carries all parameter meaning. The description adds no syntax, unit, or default guidance beyond what is already documented, so 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 (check) and resource (voltage drop on a cable run for an EV charger circuit) plus the decision rule (pass/fail at 3%). It is clearly distinguishable from check_panel_capacity and lookup_georgia_rebate by scope, though no sibling is named explicitly.

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 concrete trigger scenarios: 'long garage/driveway runs, voltage drop, upsizing wire for distance', which tells the agent when to reach for this tool. It stops short of stating when NOT to use it or pointing to alternatives like recommend_charger_setup.

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 readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the agent knows this is a safe, repeatable lookup against a fixed dataset. The description adds scope detail by naming which programs are returned, but says nothing about data currency (rebate amounts change), regional eligibility limits, or return format. Useful context, but not rich beyond what annotations provide.

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?

Two sentences, zero filler, and front-loaded: the what comes before the when. Every clause earns its place, and naming the two specific programs adds real value rather than padding.

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-parameter, read-only, no-output-schema lookup, the description is nearly sufficient: it tells the agent what data comes back at a program level, which compensates for the absent output schema. Minor gap is that it does not indicate the lookup is limited to residential/Georgia Power territory or how current the rebate figures are.

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 the single charger_type enum is fully documented in the schema, including the level2 default. The description mentions EV chargers generally but adds no syntax or behavioral detail about how charger_type changes results, so the schema does the heavy lifting and 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 (look up) and resource (Georgia EV charger rebates/incentives) and even enumerates the two programs covered, the Georgia Power EV rebate and the state tax credit. The sibling tools are electrical calculations (panel capacity, voltage drop, charger setup recommendation), so this data-lookup tool is unambiguously distinct from them.

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?

Explicitly gives the triggering condition: use when a user asks about EV charger rebates, incentives, or tax credits in Georgia. That is a clear when-to-use statement. It stops short of naming exclusions (e.g., rebates outside Georgia, commercial programs) or alternative tools, which it could have done given the sibling set.

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 readOnlyHint, idempotentHint, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description adds genuine value by disclosing the advisory nature of the output (plain-language explanation with NEC citations), though it says nothing about limits, edge cases, or unsupported configurations.

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 with zero padding: capability, trigger condition, and return contents in that order. Nothing repeats the title or wastes the agent's attention.

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 correctly carries the return contract (wire gauge, breaker amps, EGC size, explanation with citations), and the four input parameters are fully covered by the schema. 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?

Schema description coverage is 100%, so every parameter (charger_amps, run_length_ft, wire_material, ambient_temp_c) is already documented with ranges, enum values, and defaults. The description adds no parameter-level meaning beyond that, so the baseline 3 is appropriate.

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 names a precise verb and resource ('Recommend breaker size, wire gauge, conduit, and ground wire') and scopes it to 'a home EV charger circuit', which cleanly separates it from check_voltage_drop and check_panel_capacity without opening their schemas.

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 gives an explicit trigger ('Use when a user asks what wire size, breaker, or circuit their charger needs'), which is strong when-to-use guidance. It stops short of naming when-not-to-use or pointing at the sibling tools for adjacent questions (panel capacity, voltage drop).

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