See how it can look here: https://bsky.app/profile/nkiaru.bsky.social/post/3lrvofh64n225
Most AI and media services work still happens through a UI a human drives. Fiji MCP MCP server moves it into the conversation instead. See how it can look here: https://bsky.app/profile/nkiaru.bsky.social/post/3lrvofh64n225.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
The server publishes 2 tools. What each one is for:
fiji-tools — (Java) contains a few functions that facilitates the comminucation with a LLM. The GUI for coding (editor) is easily accessible to a user, but not tofiji_mcp — (Python) simple wrapper of Fiji functions, exposed as tools for the MCP serverAmong the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Fiji MCP's toolset — fiji-tools, fiji_mcp — is a fair guide to whether it matches your workflow. It is maintained by NicoKiaru; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Fiji MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| fiji-tools | (Java) contains a few functions that facilitates the comminucation with a LLM. The GUI for coding (editor) is easily accessible to a user, but not to a bot |
| fiji_mcp | (Python) simple wrapper of Fiji functions, exposed as tools for the MCP server |
{"mcpServers":
{
"fiji_mcp": {
"command": "uv",
"args": [
"--directory",
"C:/Users/Nicolas/PycharmProjects/fiji_mcp",
"run",
"main.py"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
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.