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ws_check_rim_fitment_for_vehicle
Read-onlyIdempotent

Check if specific rims (bolt pattern, diameter, width, offset) fit a given vehicle make, model, and year. Returns documented fitments or confirms no match for wheel compatibility decisions.

Instructions

Check whether specific rims fit a specific vehicle (make + model, optionally year).

Answers "will 5x114.3 17x7 ET40 rims fit my 2020 Honda Civic?" in one call: returns the vehicle's modifications (trims) where this rim appears as a documented fitment. An EMPTY result means no documented fitment for that combination — the rim is likely incompatible or undocumented.

The API has no year parameter, so 'year' is filtered MCP-side against each modification's production range (start_year/end_year); each row echoes its range so near-misses can be explained.

Prefer this over ws_search_by_rim + ws_search_by_vehicle comparison when the user names a specific vehicle.

IMPORTANT: This is a Search method — only call when a user explicitly requests a fitment check. Do not call in autonomous loops.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cbNoCentre bore in mm (e.g. 64.1)
makeYesMake slug (e.g. 'honda'). Use ws_list_makes to find valid slugs.
modeNoAxle mode
yearNoModel year — filters to modifications in production that year
limitNoResults per page
modelYesModel slug (e.g. 'civic'). Use ws_list_models to find valid slugs.
offsetNoPagination offset
regionNoRegion slug(s) (e.g. ['usdm'] or ['eudm', 'audm']).
rim_widthYesRim width in inches (e.g. 7)
rim_offsetNoRim offset ET in mm (e.g. 40)
bolt_patternYesBolt pattern of the rim (e.g. '5x114.3')
rim_diameterYesRim diameter in inches (e.g. 17)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

The description discloses key behaviors beyond annotations: an EMPTY result means no documented fitment, year is filtered MCP-side with each row echoing its production range, and each row is a modification/trim where the rim appears. It adds meaningful context without contradicting the readOnlyHint and idempotentHint annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured into short paragraphs, immediately front-loading the core purpose. Every sentence serves a purpose: example, empty-result semantics, year-filter behavior, and usage guidance. There is no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, the description still explains the meaning of results (modifications with fitment, empty result semantics) and edge cases (near-misses due to year). With 12 parameters and several sibling tools, the description is sufficient for an agent to select and call this tool confidently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining that the 'year' parameter is filtered against start_year/end_year and that rows echo the range for near-miss explanation. It also provides a concrete example of how parameters combine, though it does not delve into each parameter beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'Check whether specific rims fit a specific vehicle', a specific verb+resource combination, and illustrates with a concrete example question. It distinguishes itself from sibling tools by explicitly recommending this over ws_search_by_rim + ws_search_by_vehicle and from tire fitment tools by name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: 'Prefer this over ws_search_by_rim + ws_search_by_vehicle comparison when the user names a specific vehicle.' It also gives a clear exclusion: 'only call when a user explicitly requests a fitment check. Do not call in autonomous loops.' This provides excellent decision guidance.

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

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