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Connect local llama.cpp models to IBM Bob in VS Code.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "LocalLLM-MCP" yet — see the docs or source repo.
Using LocalLLM-MCP, connect to my configured local Granite model and answer: explain what this Python function does and any potential issues.
The AI returns a code explanation and issue analysis through the configured local model.
I have added Qwen in models.json. Switch to that model and summarize the key points of the following technical note.
The tool uses another enabled model from models.json to complete the summarization task.
I only want Gemma and Llama available to IBM Bob. Based on the current setup, tell me which models.json entries should be adjusted.
You get guidance on models.json changes to enable or disable specific models.
When using IBM Bob in VS Code, developers can connect local llama.cpp models through this MCP server and interact with models like Granite, Qwen, and Llama in natural language.
When users need to switch or disable local models frequently, they can manage availability through a single models.json file without changing code.
Teams that want IBM Bob to use local models can expose model access over STDIO with this tool and use it as a chat backend inside VS Code.
It is an MCP server that exposes local llama.cpp models to IBM Bob in VS Code over STDIO. This lets users interact in natural language with local models such as Granite, Nemotron, Gemma, Qwen, and Llama.
The description says configuration is managed through a single models.json file. You can add or disable models there without changing code.
Known prerequisites include IBM Bob in VS Code and local llama.cpp models. More specific setup steps, dependencies, or commands are not provided in the given material; see the source repository.
Run Llama models locally for private, offline AI assistance.
Securely let AI read, search, and edit local files with local LLMs.
Route coding tasks across local and remote LLMs with benchmarking and code search.
Delegate summarization, classification, extraction, and drafting tasks to a local LLM.
Offload bounded text tasks from coding agents to local or cheaper LLMs.
Let AI call localhost APIs for local development, testing, and debugging.