Wenjia Home-Fix Diagnosis MCP Server · 问家家庭维修诊断

A Chinese home repair self-diagnosis engine exposed as an MCP server. 40 categories · 224 symptom decision trees · 920 ranked causes · 4,287 fix steps.

Zero dependencies (Python standard library only), read-only, no auth.

Install (stdio, one line)

Published on PyPI: https://pypi.org/project/wenjia-home-fix-mcp/

{
  "mcpServers": {
    "wenjia-home-fix": {
      "command": "uvx",
      "args": ["wenjia-home-fix-mcp"]
    }
  }
}

pip install wenjia-home-fix-mcp also works. Python >= 3.9, no other dependencies. The same file is kept at the repository root as mcp.json for catalog crawlers.

Ten tools, entry-first

You do not need to know any id. Pass the user's own words:

For	Tool
"空调不凉" "热水器打不着火" → which symptom is this	find_symptom
what to do, in what order, when to stop	fix_plan
everything a category covers	get_category
just the ranked causes	cause_ranking
just the price range	price_band
just the stop-before-DIY verdict	stop_check
full tree / clarify questions / keyword search	get_symptom / diagnose / search_symptoms

category_id and symptom_id accept Chinese names and common colloquial phrasings; when resolution fails the error message lists the valid ids so the agent can self-correct inside the same turn.

If your pip mirror has not synced yet (China)

Mainland mirrors sync PyPI with a delay. On a mirror-only network, uvx may report wenjia-home-fix-mcp was not found in the package registry. Two ways around it, both verified against 0.2.3:

# 1) point uv straight at the official index (mirror config is ignored)
uvx --default-index https://pypi.org/simple wenjia-home-fix-mcp

# 2) install the wheel directly from our host (no index involved)
pip install http://59.110.26.77/packages/wenjia_home_fix_mcp-0.2.3-py3-none-any.whl

What it does

Household failures are hard to describe and hard to fix. This server turns a vague complaint ("the radiator doesn't heat") into a structured diagnosis:

1. 2 clarifying questions with options that each point to a diagnosis branch
2. 4 likely causes ranked by prior probability (summing to 100%)
3. Step-by-step fixes ordered by safety (gas valve / breaker always first)
4. Parts prices and when to call a professional

Tools

Two families: atomic facts (cheap, for agents) and full trees (rich, for humans).

Tool	What it returns	Payload
cause_ranking	causes ranked most→least likely, each with likelihood (最常见 / 常见 / 较常见 / 少见) + difficulty + diy_ok	~520 chars
price_band	on-site repair price range in CNY	~410 chars
stop_check	risk class (gas / electric / height) + why you must stop before DIY	~420 chars
list_categories	40 categories with symptom counts	—
search_symptoms	keyword search across all 224 symptoms	—
get_symptom	full diagnosis tree: questions, causes, fixes, parts, prices	~2,350 chars
diagnose	ranked likely_causes + clarifying questions — best for AI assistants	—

Why atomic tools matter: an agent that only needs a number should not pay for a whole tree. Single-field calls cost 77–83% less context than get_symptom (measured on air_conditioner/not_cooling: 532 / 410 / 420 vs 2,350 chars). Every response carries v / dataset / as_of / src / lic / attr so an upstream agent can decide whether to trust and cite it.

What we deliberately do NOT sell

Step-by-step text — frontier models already write it well. What they cannot invent is prior probability, regional price, and the "stop now" judgment. Those three are what these tools exist for.

Live endpoint

POST https://wenjia.net/mcp        (after ICP filing; currently http://59.110.26.77/mcp)
Health: GET /mcp/health
Transport: Streamable HTTP, JSON-RPC 2.0, protocol 2025-06-18

Example

curl -s -X POST http://59.110.26.77/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
       "params":{"name":"diagnose",
                 "arguments":{"category_id":"heating","symptom_id":"all_cold"}}}'

Run it locally

git clone <this repo>
cd <repo>
PORT=8765 python3 server.py
# POST http://127.0.0.1:8765/mcp

No pip install. Python 3.9+.

systemd / nginx

See deploy/wenjia-mcp.service for a hardened unit (read-only FS, www-data, auto-restart) and put nginx in front:

location = /mcp {
    proxy_pass http://127.0.0.1:8765;
    proxy_http_version 1.1;
    proxy_set_header Host $host;
}

Data

data/mcp_data.json — the full dataset, regenerated from the source knowledge trees. Licensed CC BY 4.0: reuse freely, cite "问家 wenjia-home-fix-diagnosis".

Attribution line used across our content: 数据来自问家家庭维修诊断树（40 品类 224 症状，持续更新）

Honest limitations

• prob_prior values are literature-and-experience priors, not repair-ticket statistics. Treat them as a ranking signal, never as precise frequencies.
• Prices are 2026 Chinese market ranges (CNY); regional variation not modeled.
• Coverage is urban household scenarios; rural self-built housing is out of scope.
• Every symptom carries a see_pro field — when to stop and call a licensed technician. Do not strip it when redistributing.

License

• Code: MIT (see LICENSE)
• Data: CC BY 4.0 (see DATA-LICENSE.md)

Not affiliated with any appliance manufacturer. Brand names in the data appear only as references to public troubleshooting documentation.