ZBS Index What actually exists in applied AI, with the source next to it

mcp server

Cardog

VIN decode, Canadian listings, market quotes, TC+NHTSA recalls. Full API: https://cardog.app/docs.md

Description as published by the maintainer. Source

  • version 2.0.1
  • slowing
  • memory and context

slowing — Most recent push to the repository was 2026-03-21. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.

What this server can do

5 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

check_recalls(ref, vin, limit, context)
The authoritative "is this vehicle under recall?" check — Transport Canada + NHTSA recall campaigns, fused and ref-keyed. Compliance guide: https://cardog.app/docs/compliance. Pass EXACTLY ONE of: - `vin` (17 characters) — the per-vehicle recall check. In the result, `resolved: false` means the VIN is not bridged into the graph yet — distinct from "no recalls" (`resolved: true, total: 0`). - `ref` — an entity ref scoping campaigns: "make:honda", "model:honda/cr-v", or "model-year:honda/cr-v/2026". A ref is `{domain}:{key}`, lowercase, with `/` separating composite key segments: "make:tesla", "model:mini/hardtop", "model-year:honda/cr-v/2026", "fuel-type:electric". (Exception: nano/squish keys are uppercase VIN charset — machine-derived, never typed from text.) Get refs from resolve_entity or identify_vehicle — never construct them from guessed names. Each campaign carries: authority (tc/nhtsa) + campaign number, component, defect/consequence summaries, the corrective action, recall date, units affected, and `affects` — the affected model-years as refs. `asOf` (VIN checks) is when the recall data was last updated, citable. Errors are instructions: every failure returns {code, message, hint, suggestions} — follow `hint` for the next call; `suggestions` lists nearest valid refs for a bad ref. Unknown-but-well-formed refs are a 400 naming the ref, NEVER a silent fuzzy match. Next: identify_vehicle({ vin }) for the vehicle's full identity; market_quote({ ref: "model-year:…" }); GET /v2/recalls/{recall-ref} for one campaign; GET /v2/recalls/feed for the newest campaigns. Required: context.
identify_vehicle(vin, context)
Decode a 17-character VIN into its full Cardog identity: canonical entity refs, the market grains (nano/squish), spec highlights, and links to adjacent resources. VIN ONLY — this tool never fuzzy-matches. If you hold free text ("2021 Civic", a make or model name), do NOT call this: call resolve_entity — free text enters the platform in exactly one tool. A non-VIN input returns a redirect hint, not a decode. A ref is `{domain}:{key}`, lowercase, with `/` separating composite key segments: "make:tesla", "model:mini/hardtop", "model-year:honda/cr-v/2026", "fuel-type:electric". (Exception: nano/squish keys are uppercase VIN charset — machine-derived, never typed from text.) The result's `refs` block (make/model/modelYear/fuelType/…) contains the join keys for every other tool; a null ref means "not derivable for this VIN", never "unknown ref". `squish` (WMI+VDS+year) is always derivable and is a valid market_quote instrument; `nano` is the fungible build grain for dedup/comparables. `specHighlights` is a best-effort skim of the canonical spec sheet (horsepower, economy, range, seating…) — the full sheet lives at GET /v2/specs/{refs.modelYear}. Errors are instructions: every failure returns {code, message, hint, suggestions} — follow `hint` for the next call; `suggestions` lists nearest valid refs for a bad ref. Unknown-but-well-formed refs are a 400 naming the ref, NEVER a silent fuzzy match. Next: check_recalls({ vin }) — outstanding recalls; market_quote({ ref: refs.modelYear ?? squish }); search_inventory({ models: [refs.model] }). Required: vin, context.
market_quote(ref, window, context)
The live market card for one instrument: quote (live listing count, best/p25/median/p75 price, average days-on-market, 30-day price cuts), a daily-bar history summary, and a bounded sample of the live listings behind the numbers. `ref` must be an INSTRUMENT ref — one of two grains: - "model-year:{make}/{model}/{year}" (e.g. "model-year:honda/cr-v/2026") — lowercase, /-separated; get it from resolve_entity (domain "model-year") or identify_vehicle's refs.modelYear. - "squish:{9 uppercase VIN chars}" (e.g. "squish:5TDGSKFCS") — the exact-config grain; get it from identify_vehicle. (squish/nano keys are the ONLY uppercase refs; every other domain is lowercase.) No other ref domain quotes. Errors are instructions: every failure returns {code, message, hint, suggestions} — follow `hint` for the next call; `suggestions` lists nearest valid refs for a bad ref. Unknown-but-well-formed refs are a 400 naming the ref, NEVER a silent fuzzy match. Optional `window` picks the history span: 1w, 1m, 3m, 6m, ytd, 1y, 3y, 5y, 10y, all. Next: search_inventory with the model's refs to walk the full book; check_recalls({ ref }) on a model-year ref; GET /v2/tape/history/{ref} for every daily bar. Required: ref, context.
resolve_entity(limit, query, domain, context)
Turn free text into canonical Cardog entity refs — THE text entry point for every other tool. A ref is `{domain}:{key}`, lowercase, with `/` separating composite key segments: "make:tesla", "model:mini/hardtop", "model-year:honda/cr-v/2026", "fuel-type:electric". (Exception: nano/squish keys are uppercase VIN charset — machine-derived, never typed from text.) Every other tool takes refs, never names. Call this FIRST whenever you hold text — "Civic", "2024 Model Y", a misspelling like "teslla" — then reuse the refs for the rest of the session. Returns candidates with confidence, best-first. `best` is the top candidate ONLY when it clears the confidence floor; otherwise it is null and YOU choose from `candidates` (or ask the user) — the API never guesses. Pass `domain` to constrain the search (use domain "model-year" when you need a market_quote instrument). Errors are instructions: every failure returns {code, message, hint, suggestions} — follow `hint` for the next call; `suggestions` lists nearest valid refs for a bad ref. Unknown-but-well-formed refs are a 400 naming the ref, NEVER a silent fuzzy match. Next steps (also echoed in each result's `next` block): search_inventory with make/model refs; market_quote with a model-year: ref; check_recalls with any make/model/model-year ref; dereference a ref (parents, children, counts) at GET /v2/entities/{ref}. Required: query, context.
search_inventory(page, sort, spec, year, limit, makes, nanos, price, models, context, odometer, fuelTypes, bodyStyles, driveTypes, vehicleTypes, transmissions, electrificationLevels)
Search live Canadian vehicle listings — ref-native. One call returns listings + facets + the total count. Filters take entity REFS from resolve_entity / identify_vehicle, never free-text names: makes: ["make:mini"], models: ["model:mini/hardtop"], fuelTypes: ["fuel-type:electric"] — plus year/price/odometer ranges and canonical spec filters, e.g. spec: {"fuelEconomyCombined": {"min": 35}, "heatedSeatsFront": ["standard"]} (numeric attrs take {min,max}; equipment attrs take ["standard"|"optional"|"unavailable"]). A ref is `{domain}:{key}`, lowercase, with `/` separating composite key segments: "make:tesla", "model:mini/hardtop", "model-year:honda/cr-v/2026", "fuel-type:electric". (Exception: nano/squish keys are uppercase VIN charset — machine-derived, never typed from text.) Errors are instructions: every failure returns {code, message, hint, suggestions} — follow `hint` for the next call; `suggestions` lists nearest valid refs for a bad ref. Unknown-but-well-formed refs are a 400 naming the ref, NEVER a silent fuzzy match. A typo'd or unknown ref 400s with code "unknown_entity_refs" naming it, with nearest-ref suggestions — correct the ref (usually via resolve_entity) and retry. Facets in the result are (ref, name, count) buckets over the MATCHING set — they double as the valid filter vocabulary for your next, narrower call. Every listing row carries its refs (makeRef/modelRef/nano). Next: market_quote({ ref: "model-year:…" }) for pricing context; check_recalls({ vin }) per listing; GET /v2/listings/vin/{vin} for the full canonical spec. Required: context.

Last successful function declaration observed on . Source: https://mcp.cardog.io/mcp?api_key={api_key}. We list what the server declared; we do not call any of these functions.

Endpoint status observed on . Source: https://mcp.cardog.io/mcp?api_key={api_key}.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Signal Value What it measures Window Observed Source
GitHub stars 1 Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. cumulative, all time GitHub
Last commit 2026-03-21 Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. point in time GitHub
Open issues 0 Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. as of fetch GitHub
Latest published version 2.0.1 Latest version string the maintainer published to the registry. as of fetch Model Context Protocol
Registry record last updated 2026-07-30 When the registry record was last updated by its maintainer. point in time Model Context Protocol
First listed in the MCP Registry 2026-07-30 Date this server was first published to the official MCP Registry. Not a usage or quality measure. point in time Model Context Protocol
repository status active The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. as of fetch GitHub
mcp tools declared 5 tools Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. as of probe mcp.cardog.io
mcp endpoint status ok The server listed 5 functions when asked. as of probe mcp.cardog.io

Where to get it

This record as data

Every field on this page, with its source and observation date, is in the catalog JSON. Fetch the whole kind at once instead of parsing this HTML.

GET /api/v1/entries/mcp_server.json

Sources

  1. cardog-ai/mcp-server on GitHub — GitHub, observed , trust tier 3.
  2. Tools declared by the MCP server at https://mcp.cardog.io/mcp?api_key={api_key} — mcp.cardog.io, observed , trust tier 1.
  3. Official MCP Registry — Model Context Protocol, observed , trust tier 1.