API Cleaner MCP
API-Cleaner-MCP helps developers and data analysts tame raw API JSON. Use clean_api_response to strip metadata noise (status codes, headers, pagination) and unwrap containers like "data" or "payload" for pure business data. flatten_json flattens nested objects to dot-notation keys (e.g., user.profile.name), and extract_fields pulls specific nested paths (e.g., items.0.id) into minimal JSON.
Overview
API-Cleaner-MCP processes raw JSON from API responses to deliver clean, usable data. It removes irrelevant metadata like status codes, headers, and pagination wrappers, while handling nested structures for straightforward downstream use. Developers save time by focusing on business logic instead of data preprocessing.
Key Capabilities
The clean_api_response tool strips noise from API outputs—such as status codes, headers, and wrappers like "data" or "payload"—to return only core business data. flatten_json transforms nested JSON into a flat map with dot-notation keys (e.g., user.profile.name) and indexed arrays (e.g., items.0.id), preserving all data. extract_fields pulls specific fields via dot-notation paths (e.g., user.profile.name), skipping missing ones and outputting minimal JSON.
Use Cases
Feed a verbose API response from a user management endpoint into clean_api_response to get just the user list for database insertion. Use flatten_json on e-commerce order data to create a simple key-value store for spreadsheet export or logging. Apply extract_fields to select invoice details like customer.name and total.amount from nested billing JSON for report generation. Chain tools to clean, flatten, and extract weather API data for a dashboard feed.
Who This Is For
Backend developers building API integrations who need quick JSON normalization. Data analysts parsing API exports for tools like Pandas or SQL. Junior engineers or scripters handling ad-hoc data cleaning without full ETL pipelines. Requires basic JSON knowledge and programming skills in languages like Python or JavaScript.