What Works Well
Tool use in AI agents has reached a level where several categories of operation work reliably. Information retrieval is the strongest category. Agents can query databases, read files, search the web, and fetch API data with high accuracy. These operations are well-suited to tool use because the agent generates structured inputs (queries, file paths, URLs) and receives structured outputs (data, content, responses).
Simple data transformation also works well. Agents can convert between formats, extract specific fields from structured data, and perform basic calculations. When the transformation is well-defined and the agent has clear examples or schemas, accuracy is high.
Code execution is increasingly reliable, especially for tasks where the agent can verify the output. Writing a Python script to process data, running it, and checking whether the output looks correct is a workflow that agents handle well because the feedback loop is tight. Wrong code produces errors or wrong outputs, which the agent can detect and correct.
Where Tool Use Struggles
Complex multi-tool workflows remain challenging. When an agent needs to coordinate calls to five different MCP servers in a specific sequence, with the output of each feeding into the next, the compound error rate becomes significant. Each tool call is a potential point of failure, and the agent's ability to recover from mid-workflow errors varies.
Tools that require precise formatting are problematic. If a tool expects a date in "YYYY-MM-DD" format and the agent provides "March 5, 2026," the call fails. Models are getting better at format compliance, but they still make formatting mistakes, especially under complex prompting conditions.
Stateful interactions are difficult. When a tool requires a sequence of calls (authenticate, then query, then page through results, then close the connection), agents sometimes skip steps, repeat steps, or lose track of the conversation state. Stateless tools that accept complete requests and return complete responses are much more agent-friendly.
The MCP Advantage
The Model Context Protocol improves tool use reliability in several ways. Standard tool descriptions help the model understand what each tool does and what parameters it expects. The protocol's structured error handling gives the agent clear information about what went wrong when a call fails.
MCP's discovery mechanism also helps. Instead of the agent needing to know about tools in advance, it can query connected servers to learn what tools are available. This runtime discovery adapts to the user's specific setup rather than relying on a fixed tool inventory.
The growing ecosystem of MCP servers means agents have access to an expanding set of tools without custom integration work. A database server, a file system server, and a web search server can be connected in minutes, giving the agent a practical toolkit for many common tasks.
Improving Tool Use Reliability
Several practices improve how well agents use tools. Clear, specific tool descriptions reduce selection errors. Including example inputs and outputs in tool descriptions helps the model generate correct parameters. Providing detailed error messages when tool calls fail gives the agent information to attempt recovery.
On the agent side, frameworks that implement retry logic, parameter validation, and output verification add reliability layers that the raw tool use capability doesn't provide. These engineering solutions compensate for the model's occasional mistakes.
Testing tools with real agents (not just with manual test cases) reveals failure modes that isolated testing misses. The way an agent formulates tool calls can differ from how a human would, and these differences sometimes expose edge cases in the tool's implementation.