Resolved

AI-generated code documentation lacks specific purpose details

Documentation
AI/ML
Code Generation
Developer Experience
Technical Writing
Python
API
Dev

Generic script overviews failed to capture the exact functionality of code examples | Problem occurred when AI-generated documentation described actions like 'connects to server' without stating the specific purpose

Issue

AI-generated documentation for FastMCP API interactions produced vague and generic overviews, failing to communicate the specific purpose of example scripts. For example, a script demonstrating FastMCP’s self-referential documentation search was described generically as ‘connecting to a FastMCP server and calling a tool,’ omitting the key detail that it was searching FastMCP’s own documentation. This led to documentation that:

  • Used generic phrases like ‘makes an API call’ without specifying endpoints or purposes.
  • Described server connections without clarifying their actual goals.
  • Failed to highlight unique or interesting aspects of examples.

Culprit

The root cause was an insufficiently strict requirement in the documentation generation prompt. The original requirement to ‘Explain WHAT this specific code demonstrates (2-3 sentences)’ allowed vague responses because:

  1. It lacked concrete examples of good or bad outputs.
  2. It did not enforce stating the specific purpose upfront.
  3. It permitted generic descriptions of technical actions without requiring clarity on the purpose.
  4. It did not reference available page context, which contained the specific purpose.

Solution

Updated Prompt Requirement

Revised the prompt to enforce specificity and clarity by requiring:

  • An immediate, specific statement of the code’s purpose in the first sentence, using a template like ‘This code…’ or ‘This example…’.
  • Concrete examples of acceptable and unacceptable outputs to guide the AI’s responses.
  • A structured template with three required sections: purpose, how it works, and why it matters.

Example Implementation

For a FastMCP self-documentation case, the output improved from:

'This code connects to a FastMCP server and makes an API call.'

to:

'This code uses FastMCP to search the FastMCP documentation - calling itself!'
How It Works: The 'Client' connects asynchronously, calls the 'SearchFastMcp' tool with a query, and returns documentation results.
Why This Matters: This demonstrates self-referential operations in FastMCP, showcasing how to build tools that interact with the documentation server.'

Key Benefits

The solution ensures:

  • Specificity: The first sentence explicitly states the exact purpose of the code.
  • Clarity: Concrete examples and structured templates guide the AI to produce relevant and accurate descriptions.
  • Context Utilization: Page context is referenced to ensure the AI extracts and prioritizes the actual purpose of the code.

This approach transforms generic documentation into precise, informative overviews that effectively communicate the purpose and significance of each example.