Get live grid carbon, power mix, and prices to schedule greener AI workloads.
This MCP tool appears to provide real-time grid data and green-time recommendations, with no required secrets or fixed remote endpoints declared, and it is open source under MIT, so no clear high-risk red flags are evident. The main cautions are that it is an executable MCP tool and the description mentions optional API keys and external data fetching, while documentation is absent and community validation is limited, so implementation details should be verified before installation.
The header states no required secrets, but the feature description mentions 'optional API keys,' indicating possible support for third-party energy data credentials. No details are provided on credential usage, storage, or scope; there is not enough evidence of obvious credential abuse, but any optional keys should be treated as sensitive.
Although the metadata says 'no remote endpoint host,' providing 'real-time electricity grid data' factually suggests access to external data sources. The materials do not list domains, data flows, or whether user input is transmitted, so there is routine uncertainty around outbound connections; however, there is no clear red flag showing exfiltration to unrelated or unknown endpoints from the provided materials.
The system checks explicitly indicate that this tool executes code. As an MCP server, this typically means running a local process to serve requests, which is an inherent capability of this tool class. The available materials do not describe elevated system operations, shell-command brokerage, or execution abilities beyond its declared grid-data purpose, so this is caution rather than high risk.
The materials do not specify which local files, databases, or system resources it reads or writes, so the data-access scope is opaque. Based on the stated functionality, it should mainly handle query inputs and return energy data, and no clearly excessive local-data permissions are disclosed; however, the missing README means actual file and resource access should be verified before installation.
Positive factors include an auditable open-source repository and an MIT license, which materially reduce risk. Points to watch are that the source is a third-party registry entry, the GitHub repo has 0 stars, maintenance status is unknown, and the README is absent, indicating weaker maturity and maintenance signals; at present this looks more like a low-visibility open-source project than something clearly malicious.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Energy Grid MCP Server" yet — see the docs or source repo.
Check the UK grid carbon intensity and green windows for the next 24 hours. Find the 3 best time slots for a power-hungry model training job, ranked by lowest carbon impact.
A ranked list of 3 recommended low-carbon time slots with carbon intensity values and brief rationale.
Fetch Germany’s current wholesale electricity price, power mix, and CO2 intensity, then briefly assess whether it is a good time to run batch inference jobs.
Current price, main generation sources, carbon intensity, and a recommendation on whether to run the job now.
Compare live grid data for the UK, Germany, and available global regions. Recommend the best region and time window for tonight’s AI batch workload, prioritizing low carbon and low electricity prices.
A ranked deployment recommendation by region and time, including carbon impact, electricity price, and overall priority.
Retrieve UK carbon intensity, forecasts, regional data, and generation mix.
Query real-time and forecast UK grid carbon intensity for emissions monitoring.
Access real-time European and GB grid data for analysis and monitoring.
Get real-time German electricity price forecasts for analysis and automation workflows.
Access Dutch energy, weather, and battery business-case data for analysis.
Get real-time electricity price signals across 40+ countries and 100+ zones.