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fix: add install instructions to MCP config and CAJAL setup section to README
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@ -76,6 +76,36 @@ User: "Set up a new compute node for the research cluster"
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| [`roomodes-example.yaml`](./roomodes-example.yaml) | Example `.roomodes` entry defining the AgentBoot custom mode |
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| [`mcp-config-example.json`](./mcp-config-example.json) | MCP server configuration snippet for the P2PCLAW server |
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## Connecting CAJAL via Ollama / OpenAI-Compatible API
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[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.
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### Using Ollama
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1. Install [Ollama](https://ollama.com/) and pull a CAJAL-supported model:
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```bash
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ollama pull cajal
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```
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2. Ollama serves an OpenAI-compatible API at `http://localhost:11434/v1` by default.
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3. In Roo Code, add an **OpenAI Compatible** API provider and set:
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- **Base URL**: `http://localhost:11434/v1`
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- **Model ID**: `cajal` (or whichever model name you pulled)
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### Using a Standalone OpenAI-Compatible Server
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If you run CAJAL through another OpenAI-compatible server (e.g., LM Studio, llama.cpp server, vLLM):
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1. Start the server and note its endpoint (e.g., `http://localhost:8080/v1`).
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2. In Roo Code, add an **OpenAI Compatible** API provider and set:
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- **Base URL**: `http://localhost:8080/v1`
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- **API Key**: leave blank for local servers, or set if required
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- **Model ID**: the model name your server exposes
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Once configured, the AgentBoot mode can leverage CAJAL for local inference during agent bootstrapping and research workflows.
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## Links
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- [P2PCLAW](https://github.com/Agnuxo1/OpenCLAW-P2P) -- Decentralized scientific research network
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@ -1,4 +1,5 @@
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{
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"_description": "Requires: npm install -g @agnuxo/p2pclaw-mcp-server (see https://github.com/Agnuxo1/p2pclaw-mcp-server)",
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"mcpServers": {
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"p2pclaw": {
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"command": "npx",
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