MCP Servers: Too Many Tools Hinder AI Agent Performance

Many users are now finding the true value of AI in productivity by pairing AI tools with their existing software. One user connected NotebookLM with various applications, from note-taking apps like Notion and Evernote to creative software like Adobe and Canva, and even other AI tools such as Gemini and Copilot. However, this pairing was often manual, requiring users to copy and paste information between apps. Model Context Protocol, or MCP, promised to automate this by allowing Large Language Models, or LLMs, to access tools directly. The user initially loaded every MCP server they could find into their coding agent. Here's the thing: every MCP server added gets dumped into the agent's memory before it even starts. This means that MCP tools consume part of the agent's context window, which is a fixed amount of text the model can hold at once. Connecting too many servers, each with its own tool definitions, quickly fills this window, leaving less space for the agent to perform useful tasks. The bottom line is that while MCP makes it easy to add tools, too many connections can hinder an AI agent's performance.