lanhu-mcp
⚡ 需求分析效率提升 200%！全球首个为 AI 编程时代设计的团队协作 MCP 服务器，自动分析需求自动编写前后端代码，下载切图
Runs with mockssource: GitHubPythonMITcommit be942fd26f55
Python, MIT licensed. The project labels itself: ai, ai agents, ai coding, ai tools, mcp, mcp service and mcp tools.
lanhu-mcp runs, with stand-ins for the services it depends on. An Argusic agent installed it in 3 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
Lanhu MCP Server 1.8.6 installed, all 216 tests pass, the server launches on HTTP port and responds with HTTP 200 to proper MCP JSON-RPC initialize requests.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 92 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- 3 minutes
- Cold machine to finish
- 7 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- pip install '.[dev]' succeeded; 216 tests passed (pytest -q); lanhu-mcp server started on port 8001, curl POST /mcp with Accept: application/json, text/event-stream returned HTTP 200 and valid initialize result
- Model tokens used
- 107,966
- Exact commit tested
- be942fd26f55
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
- Behaviour against real services
- this run was verified against stand-in services only
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
rootdir: /work/repo exec /bin/sh -lc 'cd /work/repo && . venv/bin/activate && timeout 120 python -m pytest tests/ -q --tb=sh… succeeded in 32103ms: ........................................................................ [ 33%] ........................................................................ [ 66%] ........................................................................ [100%] 216 passed in 31.44s All 216 tests passed, server launches and responds properly to MCP protocol handshake. No errors en… tokens used 107,966 All 216 tests passed, server launches and responds properly to MCP protocol handshake. No errors en…
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Installed in 3 minutes, faster than the median of the 79 comparable projects Argusic has measured.
- Nothing broke on the way: zero errors between clone and running.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 80 ai projects Argusic has installed and timed, lanhu-mcp was the 24th fastest to reach a running state, and 56 of 80 reached one at all.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.03 |
Topics (from GitHub)
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/lanhu-mcp)Questions
- Does lanhu-mcp run?
- lanhu-mcp runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 3 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test lanhu-mcp?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit be942fd26f55. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test lanhu-mcp?
- The run that produced this verdict cost $0.03: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does lanhu-mcp take to install?
- 3 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does lanhu-mcp need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing lanhu-mcp?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does lanhu-mcp compare with the alternatives?
- Of the 80 ai projects Argusic has installed and timed, lanhu-mcp was the 24th fastest to reach a running state, and 56 of 80 reached one at all.
- Where is the evidence for lanhu-mcp?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.