Developer guides
Hands-on guides for building on public social data with FetchPI: real endpoints, runnable code and the freshness/billing model explained — every link goes to a live route.
- FetchPI MCP Server: Connect Claude Desktop, Cursor or Any Agent in Five Minutes
Install the official FetchPI MCP server and give your AI agent five honest tools — platform discovery, post search, profiles, user feeds and comments — with per-call Credits billing and remediation-first errors.
- TikTok API for Developers: Profile, Posts, Comments and Search in One Request Model
How to fetch TikTok profiles, user posts, video comments and search results as normalized JSON — one API key, no scraper maintenance, with freshness control and transparent billing.
- TikTok Comments API with Python: Fetch, Paginate and Analyze Video Comments
A practical Python walkthrough: pull TikTok video comments through a normalized JSON API, paginate with sealed cursors, and hand clean records to your analysis pipeline.
- Instagram Data API Integration Guide: Profiles, Posts and Search Without Scrapers
Integrate Instagram profile, posts and search data as normalized JSON: endpoint map, pagination, freshness modes and error semantics for a maintenance-free integration.
- X / Twitter API Alternatives: Normalized Tweet Data Without Enterprise Pricing
The X API pricing tiers pushed most indie developers out. Here is what FetchPI covers — profile, tweets, replies, search, followers — with one request model and pay-per-call billing.
- Social Media Monitoring with AI Agents: Give Your Agent Structured Public Data
How to wire an LLM agent to public social data safely: normalized JSON tools, per-call billing instead of browser automation, and the request model that keeps agent loops simple.
- Cached vs Live Social Data: Latency, Cost and When Each Mode Wins
FetchPI freshness modes explained with real numbers: what cache/default/live actually do, what each costs, and how max_age turns repeat analytics reads into a fraction of the price.
- Build a Social Research Agent with FetchPI: From Keyword to Brief in One Script
A complete minimal research agent: search across platforms, expand authors, summarize with an LLM — built on normalized JSON tools with per-call accounting.
- Reddit API with Python: Posts, Comments and Search as Normalized JSON
Pull Reddit posts, comments, subreddit feeds and people search through one normalized API — real endpoints, sealed cursors, freshness control and honest billing.
- YouTube Data API Alternatives: Channel and Video Data Without Quota Anxiety
The official YouTube Data API caps you at a daily quota. FetchPI serves channels, videos and search as normalized JSON with pay-per-call billing and freshness control.
- Xiaohongshu (RED) API for Developers: Notes, Profiles and Search as JSON
Xiaohongshu data without reverse-engineering the app: profile, notes, comments and search behind one normalized API — the same request model as every FetchPI platform.
- Douyin API for Developers: Video Data Without the App Protocol
Douyin profiles, posts, comments and hashtags as normalized JSON through one API — no signature reverse-engineering, no maintenance window when the app updates.
- Social Media Sentiment Analysis: A Data Pipeline That Starts With Clean Records
Build sentiment analysis on public social data the reliable way: normalized records in, scores out — with real pagination, freshness control and per-call accounting.