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You are building developer-productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools---Read, Write, Bash, Grep, and Glob---and integrates with Model Context Protocol (MCP) servers.
After adding an MCP server with specialized code-refactoring tools---extract_function, rename_variable, and inline_function---you notice that the agent still uses basic text manipulation through Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy. Examining the configuration, you find that each MCP tool has a minimal description such as, ''extract_function: Extracts a function from code.''
What is the most effective way to improve adoption of the MCP refactoring tools?
Option C corrects the weak selection signal presented to the model. Claude chooses among available tools using their names, descriptions, parameter schemas, and the current request. ''Extracts a function from code'' does not explain whether the tool understands syntax trees, preserves imports, updates call sites, validates scope, or offers advantages over Write and sed. Anthropic identifies prompt-engineering tool descriptions as one of the most effective ways to improve agent tool use. Descriptions should state what the operation performs, when it should be selected, what inputs are required, what output it returns, and any limitations.
Option A adds a separate probabilistic routing layer without improving the tool contract Claude ultimately sees. Option B ignores the server's intended value. Option D removes a broadly useful capability and may prevent unrelated edits without guaranteeing that the MCP tools are used correctly. Each refactoring tool should instead describe its semantic behavior and contrast it with plain text manipulation---for example, that rename_variable performs scope-aware symbol renaming and updates references. Clear schemas, concrete examples, and evaluation against real refactoring tasks should accompany the improved descriptions.
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