Optimized My MCP Server

A developer optimized a local MCP server for llama-cpp, achieving 146 tokens per second with the LFM2.5-2.6B-Q4_K_M.gguf model and reducing task times from 1 millisecond to 15 microseconds through Rust optimizations. The AI agent now handles most requests in 1-3 seconds, with prompt size reduced by 7% by removing unnecessary words and merging similar tool calls.

I have mostly concluded my efforts in building and optimizing a local MCP server for llama-cpp . Making things fast is really important when designing agentic AI systems. Can you imagine if a chat bot took an hour to return a response? No one would use it—so being fast is important. Some optimizations I made: • Used Stdio instead of HTTP to reduce overhead in MCP tool calls. • Switched to a faster model—with LFM2.5-2.6B-Q4 K M.gguf I can get 146 tokens per second and still have fairly correct tool calls. • Various Rust optimizations reducing allocations, caching structs for re-use improved performance for some tasks from 1 millisecond to 15 microseconds I mostly used Microsoft Copilot . • Removed unnecessary words in tool call descriptions removed "the" words for about 7% prompt size reduction. • Merged similar tool calls insert file , insert image for additional prompt size minimization. My AI agent can handle most requests in 1-3 seconds including the actual MCP tool calls . It remains to be seen how useful my AI agent system is, but at least it has some potential to be helpful a slow system would never be helpful .