Mercedes-Benz Service Reminder – SilberArrows

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

With no annotations, the description carries the full behavioral burden. The word 'typically' hints that the answer is generic rather than VIN-specific, which is mildly useful, but there is no disclosure of what the response contains, whether it is static reference content, or any auth/caveat information.

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?

A single front-loaded sentence with the action verb first and zero filler. Every word 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 one-parameter informational lookup with no output schema and no annotations, the description is minimal but nearly sufficient to call the tool. It omits any statement about the nature or scope of the returned explanation, leaving an agent slightly under-informed about what it will get back.

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% and the enum fully documents the allowed values ('A' or 'B'). The description repeats that same A/B distinction without adding format or semantic detail beyond 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?

The description gives a specific verb+resource pair: 'Explain' + 'Mercedes-Benz Service A or Service B.' It's clearly distinct from siblings like get_next_service (scheduling) and warning_light_info (dash alerts), though it doesn't explicitly state that differentiation in the 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?

There is no guidance on when to invoke this versus get_next_service or warning_light_info, and no prerequisites or exclusions are stated. Usage must be inferred entirely from the tool name and description.

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?

No annotations are provided, so the description carries the full burden, and it discloses almost nothing behavioural: it does not say whether the result is static or fetched live, nor which contact details (phone, address, email, hours) are returned. It at least implies a side-effect-free informational lookup, but that is the extent of the disclosure.

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?

A single tight sentence with no filler, and the key fact (which centre) is front-loaded. It is a fragment rather than a sentence, but it wastes no words.

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 trivial zero-parameter, no-output-schema tool, the description is close to sufficient: it tells the agent what entity the data belongs to. It still omits what fields are returned and how the tool differs from its siblings, which the agent must guess.

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?

The tool takes zero parameters, so the baseline of 4 applies. The description adds nothing about parameters because there is nothing to add.

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 does state a concrete resource: contact details for a specific Mercedes-Benz service centre (SilberArrows, Al Quoz, Dubai). However, it is a bare noun phrase with no verb, and it sits oddly against the tool name 'find_specialist', which implies a parameterised search rather than a static, hardcoded lookup. It does not distinguish this tool from any sibling.

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?

There is no guidance on when to call this versus the sibling tools (explain_service, get_next_service, warning_light_info). The agent is left to infer that this is the contact-lookup path. No conditions, exclusions, or alternatives 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?

With no annotations, the description carries the full behavioral burden and does a good job of it: it discloses the core computation rule (alternating A/B), the default interval (15,000 km or 12 months, whichever comes first), and the inputs the estimate depends on. It does not describe the shape of the result or how partial/ambiguous inputs are handled, which keeps it short of a 5.

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, no filler. The purpose and computation basis come first and the default interval constraint is front-loaded immediately after, so the most decision-relevant facts are read first.

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 stateless estimation tool with no annotations and no output schema, the description supplies the algorithm and defaults an agent needs to call it correctly. It leaves the return value's structure implicit, which is a minor omission given there is no output schema to cover it.

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 all six parameters including the 15,000 km and 12-month defaults. The description restates those same defaults rather than adding syntax or semantics beyond 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?

The description names a specific verb (estimate) and resource (next Mercedes-Benz service) and even specifies the underlying model: alternating Service A/B, ASSYST-style, computed from last service type, date and mileage. That is precise enough that an agent knows exactly what the tool returns conceptually, and no sibling (explain_service, find_specialist, warning_light_info) overlaps with this 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?

Usage is only implied: you supply last service data to get the next service estimate. The description never states when to prefer this over alternatives or any preconditions (e.g. what to do if the last service type is unknown). Since no sibling performs a comparable estimate, the missing 'when-not' guidance is a modest gap rather than a fatal one.

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?

No annotations are provided, so the description carries the full burden. It usefully signals that content is 'general' and limited to 'common' lights, implying a static knowledge base rather than live vehicle diagnostics, but it says nothing about output format, coverage limits, or failure modes.

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 short sentences, front-loaded with the resource and immediately followed by the mode-switching instruction. Zero filler.

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-optional-parameter, no-output-schema lookup tool with no annotations, the description covers both operating modes and the scope of coverage. The only gap is the shape/format of the returned guidance, which an agent could reasonably want.

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 the non-obvious meaning of omitting 'light' entirely (returns the list of covered lights). That is semantics the schema does not express, since 'light' is not marked required.

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 resource (Mercedes-Benz warning lights and messages) and the two modes it operates in (lookup vs. list). It is clearly distinguishable from the sibling service-scheduling and specialist-finding tools, though it never names them 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?

'Omit light to list those covered' tells the agent how to trigger list mode, which is real usage guidance. However, it never says when to prefer this tool over the potentially overlapping explain_service, nor when-not to use it (e.g., for actual diagnostics or a specific fault code lookup).

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