Tool Design
The practice area in agent engineering concerned with which tools an agent can call and how their interfaces are designed. It is one of the key elements for the success of production-grade agents.
Core Facts
Agent teams that achieve results focus on three things: whether the tool design is clean, whether failure handling is adequate, and whether observability can trace the decision path.
The quality of a tool's description directly determines whether the model can select the correct tool; vague descriptions cause the model to call the wrong tool or pass incorrect parameters.
Timeline (last 90 days)
Custom tool design should follow the single-responsibility principle: one tool does only one thing, parameters are kept as narrow as possible, and returned results remain structurally stable.
Getting the maximum intelligence out of an agent is not about a magic prompt template, but the result of context engineering, tool design, and evaluation iteration working together.
Agent teams that achieve results focus on three things: whether the tool design is clean, whether failure handling is adequate, and whether observability can trace the decision path.
Before modifying the Prompt again, first read several complete records of Agent failures; usually you will find the root cause lies in tool design (misreading tools, being flooded by output, repeatedly retrying on errors it cannot understand), rather than in the Prompt itself.
Validation of parameter legality should be done inside the tool itself, rather than trusting that the model will pass in reasonable values.
Each additional tool increases the model's decision difficulty, and a large number of tools drives up the probability of functional overlap.
Destructive operations (such as delete_user) should be separate standalone tools so they can be described clearly and require approval, rather than hidden inside a tool with an action field.
All Facts (8)
Agent teams that achieve results focus on three things: whether the tool design is clean, whether failure handling is adequate, and whether observability can trace the decision path.
65%VerifiedThe quality of a tool's description directly determines whether the model can select the correct tool; vague descriptions cause the model to call the wrong tool or pass incorrect parameters.
65%UnverifiedCustom tool design should follow the single-responsibility principle: one tool does only one thing, parameters are kept as narrow as possible, and returned results remain structurally stable.
50%UnverifiedGetting the maximum intelligence out of an agent is not about a magic prompt template, but the result of context engineering, tool design, and evaluation iteration working together.
50%UnverifiedValidation of parameter legality should be done inside the tool itself, rather than trusting that the model will pass in reasonable values.
50%UnverifiedBefore modifying the Prompt again, first read several complete records of Agent failures; usually you will find the root cause lies in tool design (misreading tools, being flooded by output, repeatedly retrying on errors it cannot understand), rather than in the Prompt itself.
50%UnverifiedDestructive operations (such as delete_user) should be separate standalone tools so they can be described clearly and require approval, rather than hidden inside a tool with an action field.
50%UnverifiedEach additional tool increases the model's decision difficulty, and a large number of tools drives up the probability of functional overlap.
50%