nishati-mcp
Most AI and media services work still happens through a UI a human drives. Nishati MCP server moves it into the conversation instead. nishati-mcp.
MCP server for Kenya energy access — KPLC connections, tariffs, off-grid solar options, rural electrification programs. 5 tools.
The server publishes 1 tool. What each one is for:
Datasets — ** huggingface.co/gmahia · Docs hub: nairobi-stacknishati-mcp on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Nishati's toolset — Datasets — is a fair guide to whether it matches your workflow. It is maintained by gabrielmahia; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Datasets | ** [huggingface.co/gmahia](https://huggingface.co/gmahia) · **Docs hub:** [nairobi-stack](https://github.com/gabrielmahia/nairobi-stack) |
{
"mcpServers": {
"nishati": {
"command": "uvx",
"args": ["nishati-mcp"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.