fix: add install instructions to MCP config and CAJAL setup section to README

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Roo Code 2026-05-10 08:52:57 +00:00
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@ -76,6 +76,36 @@ User: "Set up a new compute node for the research cluster"
| [`roomodes-example.yaml`](./roomodes-example.yaml) | Example `.roomodes` entry defining the AgentBoot custom mode |
| [`mcp-config-example.json`](./mcp-config-example.json) | MCP server configuration snippet for the P2PCLAW server |
## Connecting CAJAL via Ollama / OpenAI-Compatible API
[CAJAL](https://github.com/Agnuxo1/CAJAL) is a local LLM engine used by P2PCLAW agents. You can expose it to Roo Code through any Ollama or OpenAI-compatible API endpoint.
### Using Ollama
1. Install [Ollama](https://ollama.com/) and pull a CAJAL-supported model:
```bash
ollama pull cajal
```
2. Ollama serves an OpenAI-compatible API at `http://localhost:11434/v1` by default.
3. In Roo Code, add an **OpenAI Compatible** API provider and set:
- **Base URL**: `http://localhost:11434/v1`
- **Model ID**: `cajal` (or whichever model name you pulled)
### Using a Standalone OpenAI-Compatible Server
If you run CAJAL through another OpenAI-compatible server (e.g., LM Studio, llama.cpp server, vLLM):
1. Start the server and note its endpoint (e.g., `http://localhost:8080/v1`).
2. In Roo Code, add an **OpenAI Compatible** API provider and set:
- **Base URL**: `http://localhost:8080/v1`
- **API Key**: leave blank for local servers, or set if required
- **Model ID**: the model name your server exposes
Once configured, the AgentBoot mode can leverage CAJAL for local inference during agent bootstrapping and research workflows.
## Links
- [P2PCLAW](https://github.com/Agnuxo1/OpenCLAW-P2P) -- Decentralized scientific research network

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{
"_description": "Requires: npm install -g @agnuxo/p2pclaw-mcp-server (see https://github.com/Agnuxo1/p2pclaw-mcp-server)",
"mcpServers": {
"p2pclaw": {
"command": "npx",