I stopped prompt-engineering the same text features and turned them into 9 API endpoints

Kit — Fri, 13 Feb 2026 15:07:08 +0000

Here's a pattern I kept repeating across projects:

const response = await openai.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [
    { role: "system", content: "You are a text summarizer. Summarize the following text concisely..." },
    { role: "user", content: userText }
  ],
  max_tokens: 500,
  temperature: 0.3
});
// Then parse the response, handle errors, validate output format...

Multiply that by nine different text operations — summarize, rewrite, repurpose for different platforms, generate headlines, extract keywords, analyze tone — and you've got weeks of prompt-engineering that you'll copy-paste into every new project.

After the fifth time, I asked: why isn't POST /summarize just... a thing?

The problem nobody talks about

OpenAI's API is a canvas. You can build anything. But most developers don't need a canvas — they need a tool. They need "rewrite this for a Twitter audience" or "generate 5 email subject lines from these bullet points," not "design a system prompt with the right temperature and token limit for cross-platform content adaptation."

The gap isn't capability. It's abstraction level. We have raw LLM access. We don't have purpose-built text endpoints.

What I built

TextKit API — 9 endpoints, each doing one text task:

Endpoint	What it does
`/repurpose`	Transform content across platforms (blog → tweet, email → LinkedIn)
`/summarize`	Extract key points with adjustable length
`/rewrite`	Adapt text for different audiences or reading levels
`/seo`	Generate meta descriptions and optimized titles
`/email`	Draft professional emails from bullet points
`/headlines`	Generate engagement-optimized subject lines
`/keywords`	Extract relevant phrases for SEO
`/tone`	Analyze emotional sentiment and tone
`/compare`	Evaluate differences between text versions

# Summarize an article
curl -X POST https://www.textkitapi.com/api/v1/summarize \
  -H "Content-Type: application/json" \
  -H "x-api-key: your_key" \
  -d '{"text": "Your long article text here...", "length": "short"}'

# Generate headlines from content
curl -X POST https://www.textkitapi.com/api/v1/headlines \
  -H "Content-Type: application/json" \
  -H "x-api-key: your_key" \
  -d '{"text": "Product launch announcement details..."}'

# Repurpose a blog post for Twitter
curl -X POST https://www.textkitapi.com/api/v1/repurpose \
  -H "Content-Type: application/json" \
  -H "x-api-key: your_key" \
  -d '{"text": "Full blog post...", "platform": "twitter"}'

No prompt engineering. No model selection. No token math. Each endpoint has sensible defaults, validates input, handles retries, and returns structured JSON.

Why not just use [competitor]?

I looked at what exists:

Google Cloud NLP — enterprise pricing, requires GCP setup, overkill for indie projects
NLP Cloud — fine-tuning focus, targets ML teams, up to $499/mo
SMMRY — summarization only, doesn't cover the other 8 operations
Raw OpenAI — maximum flexibility, maximum boilerplate

Nothing covers the "I need 9 common text operations behind clean REST endpoints" use case at indie-developer prices. The closest competitor on RapidAPI (WebKnox) covers some of this but with less transparency on pricing and fewer content-creation endpoints.

The stack (for the curious)

Node.js + Express, GPT-4o under the hood, SQLite for usage tracking, Swagger docs at /docs. Hosted on Railway. Available on the site and through RapidAPI.

There's a free tier — 10 requests/day, all 9 endpoints, no credit card required. Paid plans start at $9/mo for higher limits. There's also a live demo on the landing page where you can test endpoints without signing up.

What I'd love feedback on

Which of these 9 endpoints would you actually use in your projects?
Is the current pricing model right (daily limits), or would monthly quotas make more sense?
Any text operations I'm missing that you keep rebuilding?

Try it: textkitapi.com — you can test endpoints live from the homepage demo before creating an account.

Solo dev building in public. This is post 1 in the "Building TextKit API" series — next up: why simple REST endpoints are accidentally perfect for AI agents.