Building Docs Score: An API Documentation Analysis Platform

API documentation quality is inconsistent across fintech and API-first companies. Some docs are excellent, others are missing critical details, examples, or navigation patterns that make integrations slow and support-heavy. I wanted a way to evaluate documentation objectively and provide a clear path for improvement.

The Problem

Documentation issues show up as:

  • Higher support load from questions that should be answered in docs
  • Longer integration times
  • Developer frustration and churn
  • Unclear ownership of improvements

I needed a system that could score documentation quality, surface the gaps, and make recommendations that teams can act on quickly.

The Solution

Docs Score is an API documentation analysis platform that evaluates documentation across multiple quality dimensions and produces a structured report with prioritized fixes.

Analysis Dimensions

  • Completeness - Endpoints, parameters, and schemas are fully documented
  • Clarity - Examples are correct and terminology is consistent
  • Discoverability - Information is easy to find and navigate
  • Accuracy - Examples and schemas match real behavior
  • Developer Experience - Onboarding and quick starts are smooth

Technical Highlights

Architecture

  • Frontend: Next.js MVP application
  • Backend: Python FastAPI service for analysis
  • AI: Multi-LLM evaluation (Anthropic, OpenAI, Perplexity, Google)
  • CRM: Attio integration for lead tracking
  • Deployment: Railway for frontend and backend

Workflow

  1. User submits a docs URL or uploads documentation
  2. System crawls and extracts documentation content
  3. AI models analyze quality across dimensions
  4. A score report and recommendations are generated
  5. A lead is created in Attio for follow-up

Impact

  • Objective evaluation with consistent scoring
  • Prioritized fixes that align with developer experience goals
  • Reduced support burden by addressing common gaps
  • Better integrations with clearer onboarding paths

What Makes It Different

Most documentation tools focus on authoring or hosting. Docs Score focuses on analysis and improvement. It tells teams what is missing, why it matters, and what to fix first.

The multi-LLM approach improves coverage and reduces blind spots, and the scoring system makes progress measurable over time.

Tech Stack

  • Next.js, TypeScript, Tailwind CSS
  • Python FastAPI
  • Anthropic Claude, OpenAI GPT, Perplexity, Google APIs
  • Attio CRM
  • Railway

Current Status

MVP Deployed:

  • Analysis API operational
  • Frontend intake flow live
  • Attio integration working

In Progress:

  • Deeper scoring dimensions
  • Report templates and customization
  • Support for additional docs formats

What I Learned

Structured outputs are the difference between useful analysis and chaos. Multi-LLM evaluation improves coverage, but consistent scoring and clear recommendations are what make the results actionable for teams.

Links

If you are trying to improve developer experience or reduce support burden, Docs Score is built for that exact workflow.