Home repair diagnosis for AI agents: ranked causes, repair price ranges, stop-before-DIY flags.
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io.github.gmcho2009-cloud/wenjia-home-fix is an MCP server that home repair diagnosis for AI agents: ranked causes, repair price ranges, stop-before-DIY flags. Its tool list has not been published yet over stdio and http, requires no API key, and scores 60/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"wenjia-home-fix": {
"args": [
"wenjia-home-fix-mcp"
],
"command": "uvx"
}
}
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Home repair diagnosis for AI agents: ranked causes, repair price ranges, stop-before-DIY flags.
This server supports HTTP transport. Be the first to test it - help the community know if it works.
Five weighted categories - click any category to see the underlying evidence.
No known CVEs.
Checked wenjia-home-fix-mcp against OSV.dev.
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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.
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.
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.
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
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:
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.
Step-by-step text — frontier models already write it well. What they cannot invent is *prior probability, regional price, and the "stop now" judgment