Introduction
The Model Context Protocol (MCP) represents a fundamental breakthrough in AI agent evolution, enabling them to access hundreds of tools to solve real-world tasks. However, the true challenge doesn't lie in the quantity of available tools, but in their effectiveness.
Traditional software development approaches, based on deterministic systems, must evolve to support the non-deterministic nature of AI agents. When a user asks "Should I bring an umbrella today?", an agent might use a weather tool, respond from general knowledge, or even ask for location clarification.
What Are AI Agent Tools
Tools represent a new software paradigm that establishes a contract between deterministic systems and non-deterministic agents. Unlike traditional functions that always produce the same output with identical inputs, agents can generate varied responses even with identical starting conditions.
This requires fundamental rethinking in development approach: instead of writing tools like we would write functions for other developers, we must design them specifically for agents.
Development and Testing Methodology
Prototype Building
Creating a rapid prototype represents the crucial first step. It's difficult to anticipate which tools agents will find ergonomic without direct experimentation. Using Claude Code to write tools (potentially in a single session) proves particularly effective when provided with documentation for necessary software libraries, APIs, or SDKs.
Integrating tools into a local MCP server or Desktop Extension (DXT) allows connecting and testing them directly in Claude Code or the Claude Desktop app. To connect a local MCP server to Claude Code, use the command claude mcp add [args...].
Comprehensive Evaluation System
Measuring tool effectiveness requires systematic evaluations based on real use cases. Evaluation task generation should be inspired by concrete usage, relying on realistic data sources and services like internal knowledge bases and microservices.
Examples of effective tasks include complex scenarios like "Schedule a meeting with Jane next week to discuss our latest Acme Corp project. Attach notes from our last project planning meeting and reserve a conference room" rather than simplified requests like "Schedule a meeting with jane@acme.corp next week".
Principles for Effective Tools
Strategic Tool Selection
More tools don't guarantee better results. A common error consists of creating tools that simply wrap existing software functionality without considering agents' specific "affordances".
LLM agents have limited "context", while computer memory is abundant. In the case of searching contacts in an address book, instead of implementing a tool that returns ALL contacts, a targeted approach like search_contacts or message_contact is preferable.
Namespacing and Organization
With potential access to dozens of MCP servers and hundreds of different tools, namespacing becomes crucial. Grouping related tools under common prefixes (e.g., asana_search, jira_search) helps agents select the right tools at the right time.
Context Optimization
Tool implementations should return only high-value information, prioritizing contextual relevance over flexibility. Fields like name, image_url, and file_type are more effective than technical identifiers like uuid or 256px_image_url.
Collaborating with Agents for Improvement
Agents themselves can become valuable partners in identifying issues and providing feedback on contradictory descriptions, inefficient implementations, and confusing schemas. Analyzing evaluation transcripts through Claude Code allows identifying improvement areas and automatically optimizing performance.
Token Optimization and Efficiency
Implementing pagination, range selection, filtering, and/or truncation with sensible default values is essential for tools that might consume significant context. For Claude Code, responses are limited to 25,000 tokens by default.
Prompt-engineering tool descriptions represents one of the most effective improvement methods. Think about how you would describe the tool to a new team hire, making implicit context explicit and avoiding ambiguity.
Conclusion
To build effective tools for agents, we need to reorient software development practices from predictable, deterministic patterns to non-deterministic ones. Through the iterative, evaluation-driven process described, consistent patterns emerge: effective tools are intentionally and clearly defined, use agent context judiciously, can be combined in diverse workflows, and enable agents to intuitively solve real-world tasks.
FAQ
What is the Model Context Protocol (MCP) and how does it improve AI agents?
The Model Context Protocol is a framework that allows LLM agents to access hundreds of tools to solve real tasks. Unlike traditional systems, MCP establishes a contract between deterministic systems and non-deterministic agents.
How do you evaluate the effectiveness of AI agent tools?
Use evaluation tasks based on real scenarios, measuring accuracy, runtime, number of tool calls, and token consumption. Transcript analysis and agent feedback reveal improvement areas.
What are the most common mistakes in MCP tools development?
Creating too many tools, implementing simple API wrappers, not considering agent context limitations, and not optimizing responses for token efficiency.
How do you optimize tool namespacing for AI agents?
Group related tools under common prefixes by service (asana_search, jira_search) or resource (asana_projects_search, asana_users_search) to help agents select appropriate tools.
What's the difference between tools for deterministic systems and AI agents?
Tools for agents must consider LLM non-deterministic nature, context limitations, and need for clear descriptions. They require completely different design approaches compared to traditional APIs.
How can you collaborate with AI agents to improve your tools?
Use Claude Code to analyze evaluation transcripts, identify inefficient patterns, and get automatic feedback on tool implementations and descriptions for continuous optimization.