CarGene

Japanese car genealogy with sources: generations, developers, and sales figures. Read-only.

Should I use this

Quality & Safety

A
Description quality
100%
Schema completeness
93%
Naming quality
100%
Poisoning risk
100%
Permission match
100%
Protocol compliance
100%

Based on automated analysis of tool definitions and protocol compliance.

Context Cost

~1,518Tokens (tool definitions)
~1.5 KBTypical response size
Moderate attention impact (1.19% of 128k context)

This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.

Install

One-Click Install

Add this to your `claude_desktop_config.json` file:

{
  "mcpServers": {
    "genealogy": {
      "url": "https://api.car-gene.com/mcp"
    }
  }
}

Remote endpoints

https://api.car-gene.com/mcpstreamable-http

What it can do

Tool inventory

Tools (4)

🟢 Read-only🟡 Write🔴 Delete⚪ Unknown
🟢search_vehicles(query, offset)

Find series by name, maker name, or a generation's chassis code. Series names match by normalized substring (width, case, kana), maker names by prefix, chassis codes by substring of the code as written (a slash-joined code is one string, not split). Returns {query, total, hits: [{series, maker, matched_models}], next_offset} where each series and model carries its public page url; pass the ids in the hits to the other skills. Hits are ranked and cut to fit the response size; when next_offset is not null, call again with offset set to it for the rest. Empty hits means nothing matched the query.

Input Schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Series name, maker name, or chassis code. Width, case, hiragana/katakana, long vowel marks and hyphens are normalized away before matching."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "description": "Where to start in the list; pass next_offset from the previous response. Omit to start from the beginning."
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false
}
🟢get_vehicle(series_id, model_id)

Get one series with the list of its generations (models) and the cited sources, and with a model_id also that generation in detail. Returns {series, maker, models, sources}, plus model when a model_id is given. Each entry in models has id, name, chassis_code, start_year, end_year (null = still on sale), description, source_url and its public page url; the list is always the whole series. model is the named generation with drivetrains, displacements (engine displacement in cc, one row per engine), and engineers (each with role_name, source_url citing the person's own career, and link_source_url citing that person's work on this very generation; a null link_source_url means not researched yet, so do not read source_url as evidence for this generation). sources are the cited references for the descriptions (url, title, publisher, accessed_on, and model_id: null for the series overview, otherwise the generation whose description it supports); a generation's own sources come only with its model_id. For the engineers or specs of several generations, call once per model_id. An empty drivetrains, displacements, engineers, or sources list means not researched yet, never none. Unknown ids are an error, not an empty result.

Input Schema

{
  "type": "object",
  "properties": {
    "series_id": {
      "type": "string",
      "description": "Series id (a UUID from search_vehicles)."
    },
    "model_id": {
      "type": "string",
      "description": "Generation (model) id (a UUID from search_vehicles). Takes precedence over series_id when both are given."
    }
  },
  "description": "Pass exactly one of series_id or model_id.",
  "additionalProperties": false
}
🟢get_vehicle_relationships(series_id, model_id, offset)

Get the relationships between generations (succession, sibling, derivation, spiritual successor). A series_id returns the relations of the whole series; a model_id returns only the relations that have that generation at either end. Returns {series_id, url, total, relations, next_offset}, plus model_id when given: each relation has kind and kind_name, kind_directed, from_model and to_model (each with id, name, chassis_code, start_year, end_year, series_name and public page url), and where the source requires one, source_url with the verbatim source_excerpt; rationale is filled for spiritual successors. When kind_directed is false the two ends are interchangeable, so treat the relation as belonging to both generations. Relations are cut to fit the response size; when next_offset is not null, call again with offset set to it. An empty relations list means not researched yet, never that the car has no relatives.

Input Schema

{
  "type": "object",
  "properties": {
    "series_id": {
      "type": "string",
      "description": "Series id (a UUID from search_vehicles)."
    },
    "model_id": {
      "type": "string",
      "description": "Generation (model) id (a UUID from search_vehicles). Takes precedence over series_id when both are given."
    },
    "offset": {
      "type": "integer",
      "minimum": 0,
      "description": "Where to start in the list; pass next_offset from the previous response. Omit to start from the beginning."
    }
  },
  "description": "Pass exactly one of series_id or model_id.",
  "additionalProperties": false
}
🟢get_sales_figures(series_id, model_id, region_code, year_from)

Get the sales figures of a series. Without region_code it returns the overview {series_id, url, region_totals, lifetime, coverage, regions}; with region_code it returns that region year by year {series_id, url, region_code, region_totals, coverage, cells, figures, next_year_from}. region_totals sum years within one country only, so never add across countries or add a parent region to its children. lifetime rows are cumulative or per-generation totals that must not be added to the grid. With a model_id, region_totals and lifetime are those of that generation; otherwise region_totals are the series totals and lifetime is the whole series. coverage rows are the blanks that were checked and found empty, with the reason; a region with coverage rows and no figures was researched, not skipped. regions lists the region codes that have figures or checked blanks, with the first and last year; pass one of them as region_code, since any other code is an error. cells are the grid a figure may be summed into, one per series, country, calendar year and metric; a null units on a cell means the sources disagree, never zero sold. figures are the rows as published, each with units, the source's own wording, the period, source_url and a verbatim source_excerpt; match them to cells by region_code, year and metric. A model_id narrows figures to that generation, while cells stay series-wide because allocating a year to a generation needs the whole series. Years are cut to fit the response size, never splitting a year; when next_year_from is not null, call again with year_from set to it. An empty list means not researched yet, never zero.

Input Schema

{
  "type": "object",
  "properties": {
    "series_id": {
      "type": "string",
      "description": "Series id (a UUID from search_vehicles)."
    },
    "model_id": {
      "type": "string",
      "description": "Generation (model) id (a UUID from search_vehicles). Takes precedence over series_id when both are given."
    },
    "region_code": {
      "type": "string",
      "description": "A region code from regions in the overview, such as JP (upper case, as listed). Omit to get the overview."
    },
    "year_from": {
      "type": "integer",
      "minimum": 0,
      "description": "First calendar year to return for region_code; pass next_year_from from the previous response. Omit to start from the earliest year."
    }
  },
  "description": "Pass exactly one of series_id or model_id.",
  "additionalProperties": false
}

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Evidence

Recent observations

verifiedversion not recorded4 tools