MCP Tool Design: Practical Approaches and Tradeoffs

(Source: AWS Machine Learning Blog)

When designing Model Context Protocol (MCP) tools, many teams tend to directly expose existing APIs and leave it to the agents. However, this can cause context bottlenecks and errors. This article introduces the challenges of MCP tool design and practical solutions.

Context Bottlenecks and Confusion

In MCP tool design, two major problems occur: “bloat” and “confusion”.

  • Bloat: Tool definitions are always loaded into the context, consuming resources even if the tools are not used. When multiple MCP servers collaborate, there is a risk that the context will be full before the user’s question.
  • Confusion: Decreased model inference capabilities can lead to incorrect tool calls or parameter selection. Similarity between tools or ambiguity in naming can exacerbate confusion.

(Source: AWS Machine Learning Blog)

Practical Solutions: Utilizing Context Engineering

To solve these problems, context engineering is key. The following approaches are shown.

  1. Optimizing Tool Descriptions
    • Clarify the purpose and parameters of tools, and add natural language mapping.
    • Example: description: "Generate search queries and return K-12 content API"
  2. Optimizing Responses
    • Return only the minimum required fields to reduce context consumption. Provide detailed output on demand.
    • Example: default_response_fields: ["title", "summary"]
  3. Enhancing Error Handling
    • Provide clear error messages to make it easier for users to identify issues.

(Source: AWS Machine Learning Blog)

Implementation Example: Tool Comparison with Kiro CLI

AWS demonstrates MCP tool design patterns using a simulated K-12 content search API. You can test it using Kiro CLI with the following steps:

  1. Place the tool definition file locally
  2. Start the tool with the kiro run command
  3. Compare multiple design patterns and observe context usage and performance

(Source: AWS Machine Learning Blog)

Summary

  • Utilizing context engineering and optimizing tool descriptions and responses can effectively reduce bloat and confusion
  • Using Kiro CLI, you can demonstrate design patterns with simulated APIs and compare actual performance
  • When designing tools, it is essential to consider natural language mapping and on-demand responses to minimize context consumption