You're pair-programming with an AI agent. It reads your codebase, finds the function you're extending, checks the docstring, and generates code that calls it with the old parameter names. You debug for twenty minutes before realizing the docstring was updated six months ago—wait, no. The function was updated six months ago. The docstring still describes the version before the refactor.

This is the software equivalent of a recipe card that says "bake at 350°F for 30 minutes" when someone already modified the dish to be pan-fried. A cook following that card doesn't just get a mediocre result—they ruin the dish entirely. Outdated instructions are worse than no instructions, because they create false confidence.

Your AI coding agent reads your docstrings. Every time it generates code, it uses those docstrings as context for understanding your functions, classes, and modules. When those docstrings are stale, incomplete, or wrong, the agent doesn't know to distrust them. It follows the recipe card.

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The Research Confirms What You Already Suspect

This isn't just an intuition. Multiple studies have quantified how documentation quality affects LLM performance on code tasks:

• Incorrect documentation degrades LLM task success by 22.6 percentage points compared to correct docs (Macke & Doyle, 2024). Critically, incomplete or missing docs didn't cause nearly the same harm—wrong docs are uniquely toxic.
• Comment density improves code generation by 40–54% across benchmarks (Wei et al., 2024). More documentation means better AI output, but only when that documentation is accurate.
• Misleading comments reduce LLM fault localization accuracy to 24.55% (Jia et al., 2025). When the comments lie, the AI can't find bugs.
• Performance drops substantially without docstrings, and intent-aware inference is needed to compensate (Li et al., 2025).

The 2025 DORA report puts it bluntly: "AI doesn't fix a team; it amplifies what's already there." If your docstrings are accurate, AI makes you faster. If they're stale, AI confidently generates the wrong code—and you trust it because the agent seemed so sure.

Stale docstrings don't just fail to help. They actively mislead. Incorrect documentation is worse than no documentation at all.

Think of it like a pantry with mislabeled jars. A cook with an empty shelf knows to go find ingredients. A cook with a jar labeled "cumin" that's actually filled with cinnamon? That cook seasons the chili, tastes nothing wrong until it's too late, and serves a dish that's subtly, confusingly off. That's what stale docstrings do to your AI agent.

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The Gap in Your Toolchain

You probably already have docstring tooling. Most Python projects use some combination of:

• ruff (D rules) — checks how your docstrings look. Formatting, style conventions, section order.
• interrogate — checks if docstrings exist. Coverage percentage across your codebase.

These are layers 1 and 2 of docstring quality. Necessary, but not sufficient. They answer "is there a docstring?" and "is it formatted correctly?" They don't answer the question that actually matters for AI agents:

Is the docstring right?

Docstring quality has six distinct layers:

Layer	Question	Tool
1. Presence	Does it exist?	interrogate
2. Style	Is it formatted correctly?	ruff D rules
3. Completeness	Does it document all sections?	gap
4. Accuracy	Does it match the current code?	gap
5. Rendering	Will mkdocs render it correctly?	gap
6. Visibility	Will mkdocs even see the file?	gap

Layers 1–2 are table stakes. Layers 3–6 are where your AI agent's understanding lives or dies—and until now, no tool covered them.

It's like a restaurant kitchen where health inspectors check that you have a recipe binder (presence) and that the cards are legible (style), but nobody ever verifies that the recipes match what the cooks are actually preparing. The card says "sear for 2 minutes per side" but the chef switched to a 4-minute sear last month. The binder looks great. The food is wrong.

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Enter docvet

docvet fills layers 3–6 with 19 rules across four checks:

Enrichment (10 rules) — completeness. Your function raises ValueError but the docstring has no Raises: section? Your dataclass has five attributes but no Attributes: section? docvet catches it. It reads your AST—the actual code structure—and compares it against what the docstring claims to document.

Freshness (5 rules) — accuracy. This is the killer feature. docvet uses git diff and git blame to detect when code changes but docstrings don't. Changed a function's signature last week? The docstring still describes the old parameters? That's a stale-signature finding, severity HIGH.

Griffe (3 rules) — rendering compatibility. If you publish docs with mkdocs, docvet catches griffe parser warnings before they silently break your documentation site.

Coverage (1 rule) — visibility. Missing __init__.py files make entire packages invisible to documentation generators. docvet finds them.

One line to try it:

pip install docvet && docvet check --all

Example output on a real codebase:

src/pipeline/extract.py:42: stale-signature Function 'extract_text' signature changed but docstring not updated [required]
src/models/customer.py:15: missing-attributes Dataclass 'CustomerRecord' has no Attributes: section [required]
src/utils/validate.py:88: missing-raises Function 'validate_schema' raises ValueError but has no Raises: section [required]

3 findings (3 required, 0 recommended)

Each finding tells you exactly what's wrong, where it is, and whether it's required or recommended. No configuration needed to start—docvet runs with sensible defaults out of the box.

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The Feedback Loop

The tagline "better docstrings, better AI" isn't marketing—it's a literal feedback loop:

1. AI reads your docstrings to understand your code
2. docvet ensures those docstrings are complete and accurate
3. AI writes better code because its context is trustworthy
4. Repeat

If your AI agent is your sous chef, your docstrings are the recipe cards pinned above each station. docvet is the head chef who walks the line before service, pulls down every card, and checks it against what's actually in the pan. No stale cards make it to service.

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Key Takeaways

• Your AI coding agent reads your docstrings as primary context for understanding your codebase—stale docs mean bad AI output
• Incorrect documentation is worse than no documentation—research shows a 22.6 percentage point drop in LLM task success with wrong docs
• Existing tools cover style and presence (layers 1–2) but not completeness, accuracy, rendering, or visibility (layers 3–6)
• docvet fills the gap with 19 rules across four checks: enrichment, freshness, griffe, and coverage
• Freshness detection uses git history to catch code-docstring drift—no manual review needed
• One command to try it: pip install docvet && docvet check --all

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Further Reading

• docvet on GitHub
• docvet on PyPI
• docvet Documentation
• Testing the Effect of Code Documentation on LLM Code Understanding (Macke & Doyle, 2024)
• Code Needs Comments: Enhancing Code LLMs with Comment Augmentation (Wei et al., 2024)
• Assessing the Impact of Code Changes on Fault Localizability of LLMs (Jia et al., 2025)
• Your Coding Intent is Secretly in the Context (Li et al., 2025)