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AyushRajSinghParihar

flatfind

by AyushRajSinghParihar
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Python
Remote

flatfind — codex-style CLI for Bangalore house hunting (NoBroker)

flatfind takes NoBroker's real API, wraps phone+OTP login, lets you describe what you want in plain English ("2bhk in koramangala under 30k"), searches automatically, and scores photos GOOD/AVERAGE/BAD with whatever vision model you have (Claude / OpenAI / Gemini / offline heuristic).

Built to be driven by humans in a terminal AND by agents (Claude Code / Codex / OpenCode) via --json + a zero-dep MCP server.

Install

From PyPI (once published — see below):

pipx install flatfind        # recommended: isolated CLI install
# or
pip install flatfind
# or run without installing:
uvx flatfind search "2bhk in koramangala under 30k"

From source:

git clone https://github.com/ayushrajsinghparihar/flatfind.git
cd flatfind
uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"
# or: pip install -e ".[dev]"

Related MCP server: RentSmart MCP

60-second tour

flatfind localities
flatfind search "2bhk in koramangala under 30k" --limit 10
flatfind show 8aa99668 --json
flatfind analyze 8aa99668
flatfind probe-params --locality koramangala   # what the API accepts/rejects

Auth (phone + OTP — real NoBroker flow)

Search works without login. Login unlocks owner contact numbers.

flatfind auth send-otp --phone 98765XXXXX
flatfind auth login --phone 98765XXXXX --otp 123456
flatfind auth status
flatfind auth logout

Endpoints (reverse-engineered, see docs/REVERSE_ENGINEERING.md):

step

method + endpoint

params

send OTP

POST /api/v2/account/otp/send

phone (+opt otpAPP)

login

POST /api/v1/account/login/otp (fallback /api/v2/user/login/otp)

phone, otp

search

GET /api/v3/multi/property/RENT/filter

searchParam (base64 localities), city, pageNo, radius, rent, bhk, buildingType, orderBy, ...

photos

https://images.nobroker.in/images/<id>/<file>

from photos[].imagesMap

Photo analysis — depends on YOUR model

env present

backend

quality

ANTHROPIC_API_KEY

Claude Sonnet vision

best

OPENAI_API_KEY

GPT-4o-mini vision

cheap + good

GEMINI_API_KEY / GOOGLE_API_KEY

Gemini Flash vision

good

none

offline heuristic (count/resolution/owner-upload/metadata)

honest filter

flatfind analyze <id> --model auto      # default: best available
flatfind analyze <id> --model heuristic # force offline

Use from Claude Code / Codex / OpenCode

Option A — CLI: agents just run the commands above with --json. Full playbook in skills/SKILL.md (drop it into your agent's skills dir).

Option B — MCP server (zero extra deps):

// .mcp.json (Claude Code) — adapt for Codex/OpenCode
{ "mcpServers": { "flatfind": { "command": "flatfind", "args": ["mcp"] } } }

Tools: search_flats, show_flat, analyze_flat, auth_status, probe_params.

Two-layer rule: analyze filters, the host agent re-reads top photo URLs with its own eyes and makes the final call.

Repo layout

src/flatfind/
  cli.py               Typer CLI (search/show/analyze/auth/probe/mcp)
  config.py            paths, endpoints, session store
  nobroker/
    auth.py            send_otp / login_with_otp / status
    search.py          searchParam builder + paginated search
    probe.py           accepts-vs-rejects param prober
    detail.py          photo URL builder + summarizer
    localities.py      bundled Bangalore lat/lon table
    client.py          session + headers + cookie persistence
  agent/
    planner.py         NL -> filters (offline regex, reproducible)
    scorer.py          vision (claude/openai/gemini) + heuristic fallback
  mcp/server.py        dependency-free MCP over stdio
skills/SKILL.md        agent playbook
docs/REVERSE_ENGINEERING.md  how the API was mapped
tests/                 offline unit tests (no network)

Dev

python -m pytest -q
flatfind search "2bhk in hsr layout under 28k" --limit 5 --json | head -50

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