docs: define competitive positioning against pydoclint and landscape

[Alberto-Codes]

Summary

Document docvet's competitive positioning in the Python docstring tooling landscape and make it clear in the README, docs site, and PyPI description where docvet wins.

The landscape

Tool	What it does	Layers covered
interrogate	Docstring presence checking	Layer 1 only
ruff D rules (pydocstyle)	Docstring style/formatting	Layer 2 only
pydoclint	Docstring accuracy (param/return/raise matching)	Partial layer 3
darglint	Docstring accuracy	Deprecated, very slow
docvet	Completeness + Accuracy + Rendering + Visibility	Layers 3-6

docvet's unique position

docvet is the only tool that covers the six-layer docstring quality model:

1. Presence (interrogate)
2. Style (ruff D rules)
3. Completeness — enrichment checks (10 rules)
4. Accuracy — freshness checks via git (5 rules)
5. Rendering — griffe compatibility for mkdocstrings (3 rules)
6. Visibility — coverage for mkdocs discoverability (1 rule)

No other single tool covers layers 3-6. Pydoclint overlaps partially on layer 3 but lacks freshness (git-based staleness detection), rendering (griffe), and visibility (mkdocs coverage).

The narrative

"Docstrings are becoming the interface between your code and AI agents. Docvet is the only tool that validates all six layers of docstring quality — from completeness to rendering."

Deliverables

• Comparison table in README or docs site
• "Why docvet?" section addressing the landscape
• Differentiation from pydoclint specifically (the closest competitor)
• Positioning for AI-assisted development: "docvet catches what AI agents break"

References

• pydoclint
• interrogate
• Beyond Human Eyes: Docstrings as AI Interface
• The LLM Flywheel Effect

Related

• docs: add AI Agent Integration section to README #153 (README AI section — positioning belongs here or adjacent)
• chore: audit and expand GitHub/PyPI discoverability metadata #156 (Discoverability — clear positioning helps SEO and community list submissions)