mcpField
F
fiber

fiber

Local
fiber.acCloud & Infra
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About fiber

Optical ops MCP for AI DCs: gNMI, ML RUL, Digital Twin, NCCL, SPARQL semantic

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "liveplex-cpu-fiber": {
      "command": "uvx",
      "args": [
        "fiber-mcp"
      ],
      "env": {
        "FIBER_API_URL": "<FIBER_API_URL>",
        "FIBER_API_KEY": "<FIBER_API_KEY>",
        "FIBER_ENABLE_WRITE": "<FIBER_ENABLE_WRITE>",
        "FIBER_MCP_TOOLS_ALLOWLIST": "<FIBER_MCP_TOOLS_ALLOWLIST>",
        "FIBER_HTTP_TIMEOUT": "<FIBER_HTTP_TIMEOUT>"
      }
    }
  }
}
Run commanduvx fiber-mcp
Environment variables to set
FIBER_API_URLBase URL of the FIBER deployment (e.g., https://api.fiber.ac or http://localhost:8000 for self-hosted).
FIBER_API_KEYSecretBearer token for the FIBER API. Role determines which tools work: viewer (24 read tools), operator (+write), admin (all).
FIBER_ENABLE_WRITESet to 1 to expose write tools: demo trigger, NCCL ingest, RMA create. Defaults to read-only for safety.
FIBER_MCP_TOOLS_ALLOWLIST(v1.0.2) Optional CSV of tool names. When set, only these tools are exposed — letting operators scope autonomous agents to a minimum surface (e.g. 'fiber_health,fiber_cluster_overview,fiber_list_alerts'). Empty = all tools exposed (default).
FIBER_HTTP_TIMEOUTPer-request HTTP timeout in seconds. Increase if running Digital Twin Monte Carlo with iterations>1000.

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypifiber-mcp1.1.0

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

The maintainer has not added details yet. Check the README in the repository.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.