Anthropic and MCP: why AI agents lose focus when given too many tools

MCP didn't disappear and didn't fail as a standard. But the idea of connecting all tools, databases, and APIs to an AI agent at once turned out to be a dead end: instead of expanding capabilities, the model loses focus and wastes context on noise.

When More Is Worse

A year ago, MCP looked like a universal answer to the chaos of integrations. One protocol promised to connect AI agents to GitHub, Slack, Jira, internal databases, and any other external systems without a zoo of custom plugins. The industry quickly embraced this logic: by the end of 2025, MCP was already supported by ChatGPT, Cursor, Gemini, and VS Code, and the number of servers exceeded 10,000, according to the author. On paper, it looked like a mature ecosystem ready to become the base layer for a new generation of agent products.

The problem surfaced when teams started taking the most obvious step: connecting all available tools to the agent at once. Instead of benefit, this created overload. Before the first user request, API schemas, function descriptions, call parameters, and service instructions for code, documentation, bug tracking, and analytics flooded the model window. In some cases, tool initialization alone consumed up to 55,000 tokens. For a regular program, this is no problem, but an LLM has to read all this volume, choose the right tool, and hold the result in memory without losing the thread of the task.

"Context is limited and doesn't become more useful simply from volume."

Three Working Patterns

A new approach quickly emerged from this: standardization of access alone guarantees nothing if the model sees too much irrelevant information. So the focus shifted from the question "what can be connected" to "what exactly should the agent see right now."

In practice, in 2026, three patterns took hold that reduce cognitive load without abandoning MCP as an integration layer.

• Tool search. The agent first searches for a suitable tool in the catalog, loading the schema only for the current step.
• Agent Skills. Instead of raw API access, the model receives a ready-made skill with a narrow task, such as creating a bug report from logs.
• CLI wrappers. Instead of parsing cumbersome JSON schemas, the agent calls a simple command in the terminal with understandable parameters.
• Unloading after a step. Tools and instructions are kept in context only while they're actually needed, then removed.

Each option has a trade-off. Searching for tools adds an extra call and latency, skills limit the model's freedom, while the CLI approach sacrifices flexibility for reliability. But all three schemes beat the strategy of "give the agent everything at once" because they save the scarcest resource — the model's attention. The less noise, the better the chance that the agent will choose the right action and not go off track halfway through.

The Price of Convenient Integration

The main takeaway of the article is that MCP solves the compatibility problem but doesn't solve the thinking problem. When an agent has dozens of similar tools, it must not just know of their existence, but compare options, understand the purpose of each, select parameters, match the response to the task, and not forget intermediate results. The wider the catalog of possibilities, the higher the risk of false positives, failed calls, and meaningless reasoning on top of irrelevant context.

Therefore, the degradation in quality here is not due to the protocol itself, but to the architecture built on top of it. Hence the interest in Agent Skills, which Anthropic promotes. They raise the level of abstraction: instead of low-level API operations, the agent receives a packaged workflow with pre-defined logic. In this mode, the model doesn't solve anew how to communicate with Jira or another system, but uses a ready-made route for a specific business task. This reduces token consumption, decreases the probability of error, and makes agent behavior more predictable. For product teams, this is an important shift: universality is no longer considered an unconditional advantage if it breaks stability.

What This Means

For teams building autonomous systems in 2026, the question is no longer about the number of connected tools. The stakes shift to routing, context hygiene, and narrow, reliable workflows. MCP remains a useful integration standard, but the advantage goes to those who show the model the absolute minimum of data and functions at the right moment, rather than the entire catalog of possibilities all at once.