🦀 Rust API, 🐍 Python API, and 🤖 MCP Server for TP-Link Tapo smart devices
C-Rank Grade: B (Solid) - 53/100
External PR Merge Rate: 38%
Response Time: 3d
First Timer Success: 100%
mihai-dinculescu/tapo has a recorded first-timer success rate of 100.0%. Repositories ranked B typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in mihai-dinculescu/tapo respond to incoming external pull requests in approximately 82.4 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 52.9 places mihai-dinculescu/tapo in the B tier.
The external contributor pull request merge rate for mihai-dinculescu/tapo is 37.5%, based on public PR activity from non-core contributors.
mihai-dinculescu/tapo currently has 3 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Growing Rust project in mihai-dinculescu/tapo welcoming community pull requests and bug fixes.
Average Response Latency
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External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
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Show contributors that your repository actively reviews and merges external pull requests.
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Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 20:00 UTC (20:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 17:00 to 21:00 UTC (17:00 - 21:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~54 hours (47% approval). Large PRs (>500 lines) take ~396 hours (17% approval). Keep your diff compact for rapid review.
Summary The MCP server can turn energy-monitoring plugs on and off, but it can't read their history. The tapo library already supports this through get_energy_data and get_power_data, so this is mostly wiring. Proposal Add two get capabilities to get_device_state: EnergyData: energy used per hour, day or month (Wh), via get_energy_data. PowerData: power draw per 5 minutes or per hour (W), via get_power_data. Supported devices: P110, P110M, P115, and the P304M and P316M child plugs. Notes The device only accepts certain date ranges (see the docs on EnergyDataInterval and PowerDataInterval), and the library doesn't check them. Shape the inputs so a bad range can't be passed, e.g. Daily takes a year and quarter instead of a start date. get_energy_data interprets dates in the server's local time zone, and the Docker image runs in UTC unless TZ is set. Use the existing TriggerLogs capability as a template. It takes parameters and is wired through models.rs, requests/get_devices.rs and to
I was looking to get into smart home-ing and the Tapo L430C seems to be a good starting point. I could not find any mention of this model in the documentation or in the planned TODO, so making this issue. Amazon: https://amzn.eu/d/0fAXcvkY I will test this crate with my device in the upcoming week, if it works OotB, this issue can be closed with a simple documentation change.
Using the P100 python example I tried to connect to my P100, which is running very old firmware (1.2.10 Build 20210207 Rel. 67438) Attempting to run the example, I get an InvalidPublicKey error. I tried to turn on extra debug logging but couldn't find anything - can someone recommend debug steps? (venv) jammy@pi:~/tapo $ python3 tapo_p100.py Traceback (most recent call last): File "/home/jammy/tapo/tapo_p100.py", line 34, in <module> asyncio.run(main()) File "/usr/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/asyncio/base_events.py", line 653, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/home/jammy/tapo/tapo_p100.py", line 15, in main device = await client.p100(ip_address) ^
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When evaluating whether to contribute to mihai-dinculescu/tapo, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of mihai-dinculescu/tapo acknowledge new external contributions in approximately 3 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 37.5% were successfully merged into the primary branch.
mihai-dinculescu/tapo has a recorded first-timer success rate of 100.0%. Repositories ranked Solid typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~3 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 52.9 places mihai-dinculescu/tapo in the Solid tier.
The external pull request merge rate is 37.5%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, mihai-dinculescu/tapo currently has 3 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for mihai-dinculescu/tapo are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.