When Model Context Protocol (MCP) tools underperform, the cause is rarely the protocol itself but the tool design. Many teams start by exposing an existing API as-is and trusting the agent to figure out the rest. It is a natural way to extend APIs to agentic systems and generative AI coding tools. For straightforward use cases, it can work. But often it does not.
You must design your tools for how large language models (LLMs) and agentic systems work. Without this, you risk failed tool calls, wrong parameter values, and retries that waste context and degrade performance. In this post, we show where MCP tool design goes wrong and how to fix it with practical context engineering approaches.
Two problems are behind most of these failures. The first is bloat. Tool definitions load into the LLM’s context on every call, whether the tool is used or not. Multiple connected MCP servers can consume significant context before the user has asked a single question. As context fills, an LLM’s ability to reason can degrade and cause the session to become less productive.
The second is confusion. As reasoning degrades, the LLM makes poorer choices, calls the wrong tool and chooses incorrect parameters. Subsequent retries compound the issue by further contributing to bloat. Semantic similarity between tools, too many options, and ambiguous naming also contribute to confusion. A common solution is to enrich the tool descriptions with clearer definitions, natural language mappings, and usage examples. This does help with confusion. But everything added risks worsening bloat and compounding the very issue you are working to solve.
