Collector-car values from completed auction sales: median, 25th-75th band and sale count by model.
Scored across 2 tools
list_models enumerates available models while value_model returns a valuation for a specific model+year range — clearly different actions on different granularities. The descriptions even encode the precedence rule (call list_models first if unsure), eliminating misselection risk.
Both names follow the same verb_noun snake_case pattern: list_models and value_model. The verbs are distinct and meaningful, and the noun convention (plural for enumeration, singular for a single lookup) is applied correctly.
Two tools is thin, sitting at the borderline the rubric calls out for 1-2 tool surfaces. However, the domain (a read-only published valuation dataset) is genuinely narrow, so the small surface is defensible rather than an obvious mismatch.
The read path is well covered: enumerate models with medians/bands, then value a model over a year range with provenance and as-of date. Minor gaps exist — no filtering/search by make, year, or price band, and no way to list the year ranges a given model covers — but agents can work around these via list_models.
Every collector-car model BidBetter publishes a value for, with its median, band, sale count and page URL. Call this first when you are unsure whether BidBetter covers a car, rather than calling value_model and guessing.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful context beyond them: this is an exhaustive coverage catalog rather than a per-car price lookup, and it discloses the four return fields (median, band, sale count, page URL) despite there being no output schema. It stops short of describing entry volume, pagination, or filtering behavior for a large catalog, which keeps it from 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, zero filler: the first front-loads what the tool returns, the second front-loads when to call it and which sibling to avoid. Every clause 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 zero-parameter, read-only listing tool with annotations already covering safety and closed-world scope, the description carries everything an agent needs: what the catalog contains, what each entry yields, and when to prefer it over value_model. The absence of an output schema is offset by the in-text field enumeration.
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 is 4 and the description correctly implies a no-argument, list-everything call. Nothing in the schema needs compensating for here.
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 resource (collector-car models BidBetter publishes a value for) and enumerates the returned fields (median, band, sale count, page URL). It clearly distinguishes this coverage-catalog lookup from the sibling value_model lookup, so an agent can tell the two apart without opening either schema.
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 ('Call this first when you are unsure whether BidBetter covers a car') and explicitly names the alternative to avoid ('rather than calling value_model and guessing'). The when-to-use condition and the competing tool are both stated, 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.
Median, 25th-75th percentile band and sale count for a specific collector-car model and year range, from completed auction sales. Use for questions like 'what is a 1989 Porsche 911 worth' or 'how much is a 1969 Corvette C3'. Returns one sentence you can quote plus the numbers behind it, with an as-of date and a link to the method. It covers a model and year range, not one individual car.
| Name | Required | Description | Default |
|---|---|---|---|
| make | No | Optional exact make, e.g. 'Porsche'. | |
| query | Yes | Make, model or year range, e.g. '911', 'Corvette C2', 'Bronco', 'Mustang 1964'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish a safe read-only, closed-world lookup, so the bar is lower, and the description still adds real context: the data source (completed auction sales), the return contents (a quotable sentence plus the underlying numbers), an as-of date, and a link to the method. That provenance and freshness detail is exactly the kind of information annotations cannot carry. It does not say what happens when a model has too few or no sales, which is the main undisclosed edge case.
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 output shape is front-loaded in the first clause, then usage, then return contents, then the scope caveat. The example questions are compact and earn their place by mapping natural-language phrasing to the tool. No sentence is 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?
With no output schema, the description carries the burden of describing returns, and it does so (quotable sentence, numbers, as-of date, method link). For a two-parameter read-only lookup with full schema coverage, that is nearly everything an agent needs. The only omission is behavior on sparse or zero-sale models.
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 both parameters are already documented, and the description adds only the framing that the target is a model/year range rather than an individual car. It does not explain how the optional 'make' interacts with a 'query' that already contains a make, which is the one ambiguity an agent could stumble on. Baseline 3 applies when the schema does the heavy lifting.
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 resource and output (median, 25th-75th percentile band, sale count) for a specific scope (collector-car model + year range, from completed auction sales), which is far beyond restating the name. It clarifies that it prices a model cohort rather than an individual car. It stops short of explicitly contrasting with the sibling list_models, so the agent must infer the split between 'list available models' and 'value a model'.
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?
"Use for questions like 'what is a 1989 Porsche 911 worth'" gives a concrete triggering intent, and "covers a model and year range, not one individual car" states a scope exclusion. No sibling alternative is named, so the agent gets clear context but no explicit routing rule versus list_models.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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