Leboncoin — recon plan (FR marketplace)

Not a social graph — a marketplace: the "nodes" are Ads and Sellers,
the edges are seller↔listings, category/location facets, and messaging. Same
recon method (find the API → auth from cookies → typed queries → playbooks).

Internal API: https://api.leboncoin.fr/... (the web app calls it
directly):

• Search: POST https://api.leboncoin.fr/finder/search — JSON body with
  filters (category, location {region/department/city}, keywords, price range,
  attributes), limit, offset, sort_by. Returns ads[] + facets.
• Ad detail: GET https://api.leboncoin.fr/api/adview/v1/public/<listId>
  (or the listing page hydration) — price, description, images, attributes,
  seller.
• Seller: the seller's other listings (search by owner/user_id),
  pro/private, ratings.
• Messaging: the conversation API (login required).

Auth (from the page)

• api_key header — a public web key Leboncoin's JS sends on every call
  (capture from a request header).
• cookies ride same-origin, including the datadome cookie — Leboncoin is
  behind DataDome anti-bot (like a WAF). Because we run in the REAL browsing
  session, the valid datadome cookie is already present → page-context fetch
  passes. (This is the gate, analogous to TikTok's signing.)

In-page fetch:

async body => {
  const r = await fetch("https://api.leboncoin.fr/finder/search", {
    method: "POST",
    headers: {
      "content-type": "application/json",
      api_key: "<captured web api_key>",
    },
    body: JSON.stringify(body),
  })
  return await r.json()
}

Entity model

Ad{ list_id, subject, body, price, category, location{city,zipcode,lat,lng}, images[], attributes[], owner:Seller, index_date }
· Seller{ user_id, name, type: private|pro, siret?, no_of_ads, ratings? } ·
Category{ id, name }.

Recipes (read-first; writes gated)

• Search listings: finder/search with filters → ads (price, location,
  seller).
• Ad detail: adview → full ad + seller + images.
• Seller's listings: search filtered by the owner/user_id → their inventory
  (the "persona" of a seller = their listings + pricing + activity).
• Market scan / price graph: search a category+area → aggregate prices,
  freshness, sellers → a market view (the marketplace analog of the social
  graph).
• Discovery → outreach: find matching ads → (gated) message the seller.

Capture method

Open leboncoin.fr, run a search/open an ad → list_network_requests for
api.leboncoin.fr → read the endpoint, the JSON body shape, and the api_key
header. Confirm the datadome cookie is set (else solve the challenge in the UI
first).

Gotchas

• DataDome: bursts trigger a challenge/captcha. Keep volume low +
  human-spaced; if a request 403s with a DataDome body, solve it once in the UI
  (the cookie refreshes) then resume.
• Body filter schema is nuanced (category ids, location objects) — capture a
  real UI search's body and parameterize from it.
• Writes (message seller, post ad) need login + may add their own tokens —
  capture live.