Why does hooking an AI agent up to tools through MCP sometimes make it flaky instead of reliable in real apps?

This explores why routing an agent's tool use through MCP can make production systems behave unpredictably, and what the corpus says the fix looks like. One caution first: the corpus has a single source that looks directly at MCP, so the rest of this answer draws on related work about function calling and agent reliability. That direct source puts the problem in a specific place. The failures came from MCP adding a layer where the model had to choose from a menu of tools and work out the parameters for each call. Both steps are guesses, and guesses can come out differently from one run to the next. The team got predictable behavior back by removing that layer. They switched to explicit direct function calls and gave each agent exactly one tool. They weren't alone in avoiding frameworks: in a survey of 306 practitioners, 85% of production teams said they build custom agents instead of using frameworks Why do protocol-based tool integrations fail in production workflows?.

The wider literature on function calling suggests MCP is mostly exposing older weak points. One analysis finds three separate places where tool calling breaks. First, finding the right tool by similarity search gets unreliable as the tool list grows. Second, putting every tool's full description in the prompt bloats it and weakens the model's reasoning. Third, models trained on ordinary free-flowing text have trouble producing rigid structured output such as JSON. Fixing one of these leaves the other two in place Where do traditional function calling systems actually break down?. A protocol that offers a large catalog of tools at runtime hits the first two problems head-on.

The obvious workaround doesn't help much. Setting temperature to zero makes the model give the same answer every time, but that answer is still one sample from its range of possible outputs. Consistent is not the same as correct Does setting temperature to zero actually make LLM outputs reliable?. If the model reliably picks the wrong tool, a zero temperature only makes it reliably wrong.

The corpus keeps pointing toward one design idea: give the model fewer decisions to make. MAKER breaks tasks into tiny steps and has several attempts vote on each one. With steps that small, even modest models can run a million steps with zero errors Can extreme task decomposition enable reliable execution at million-step scale?. ReWOO-style systems have the model write its whole plan before calling any tools, so tool results can't pull its reasoning off course partway through Can reasoning and tool execution be truly decoupled?. The "one tool per agent" fix fits the same pattern.

The part you might not have expected: several notes argue that some guarantees can't come from inside the model at all. Instructions in the prompt can't guarantee that an agent will stop rather than loop forever. That takes an outside supervisor with hard timeouts Can prompt alignment alone guarantee agent termination in loops?. In the same way, checking one action at a time can't catch a problem that only shows up across a sequence of individually allowed actions Can stateless checks ever catch sequence-level constraint violations?. The MCP story follows this pattern. When you need predictable behavior, put the decision in ordinary code outside the model instead of asking the model to make it reliably.