Unlocking Agentic Potential: Best Practices for Building AI Tools from Anthropic

Innovative AI agents are transforming workflows, but their effectiveness relies heavily on the quality of tools crafted for them. As systems powered by large language models like Claude and Codex become more advanced, developers must evolve their approach to tool creation, prioritizing flexibility, clarity, and iterative improvement. Here we review guidance from Anthropic on building effective tools for your AI clients. 

From Prototype to Production: Building Effective Tools

• Prototyping: Begin with rapid iterations using documentation tailored for language models. Wrapping tools in protocols such as the Model Context Protocol (MCP) enables swift local testing. Real user feedback and observation are crucial for uncovering friction points early in the process.
  
  
• Comprehensive Evaluation: Evaluate tools with realistic, complex tasks rather than simplistic prompts. Use agents to both execute and assess these scenarios, ensuring evaluations mimic practical, multi-step workflows agents will encounter.
  
  
• Iterative Improvement: Harness agent feedback and usage metrics to enhance tools over time. For instance, Claude Code can review interaction transcripts and suggest refinements, making every evaluation cycle an opportunity for optimization.

Principles for Crafting High-Quality Tools

• Purposeful Tool Selection: Choose functions that genuinely improve agent workflows. Avoid superficial API wrappers; instead, focus on consolidating related processes and ensuring each tool addresses a distinct, meaningful purpose.
  
  
• Thoughtful Namespacing: Organize tools under logical prefixes or suffixes to avoid ambiguity, especially as the tool ecosystem grows. Clear namespacing reduces confusion and sharpens agent understanding of each tool’s role.
  
  
• Return Relevant Context: Deliver only high-value, contextually rich information. Use natural language identifiers and offer flexible response formats, which help agents avoid hallucinations and improve precision.
  
  
• Optimize for Token Efficiency: With limited context windows, tools should include features like pagination and filtering. Actionable error messages and concise output formats minimize resource waste and prevent information overload.
  
  
• Prompt-Engineer Descriptions and Specs: Write tool documentation as if onboarding a new team member, make assumptions explicit and clarify all inputs and outputs. Even minor improvements in descriptions can dramatically reduce agent errors and improve performance.

Collaborating with Agents for Tool Evaluation

Agents are not just tool users, they can also help refine and evaluate tools. By analyzing an agent’s reasoning, feedback, and tool interaction patterns, developers can spot redundancies, bottlenecks, or unclear instructions. Agents may even restructure tool groups for greater consistency if given access to evaluation data.

Quantitative metrics such as accuracy, runtime, error frequency, and token usage, reveal where optimizations are needed most. Combining agent insights with human oversight ensures tools evolve in sync with real-world needs and agent capabilities.

Future-Proofing Tools in a Rapidly Evolving Landscape

Building for AI agents demands a shift from deterministic software development to more adaptive, context-aware designs. The most effective tools are robust, clearly documented, and scalable across diverse tasks. As language models and agent protocols advance, systematic evaluation and continuous refinement will keep tools relevant and impactful.

For those seeking deeper expertise, Anthropic provides courses and documentation on API development, MCP, and Claude Code. Embracing a culture of continuous learning and iteration will be essential for staying ahead in agent tool development.

Source: Anthropic Engineering Blog