A
licenseB
qualityC
maintenanceParse any file or URL into structured text. Extract text from PDF, DOCX, YouTube, web pages, images, and 25+ formats via one API. Tools: parse_url, parse_file, get_youtube_transcript.
3
2 npm
MIT
content-core
by lfnovo
Related Servers
Alternatives to content-core
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceEnables AI editors and agents to read public web pages as clean Markdown and convert URLs, HTML, or Markdown into PDF, Word, Markdown, HTML, or JSON documents.MIT
- AlicenseNot gradedqualityDmaintenanceConverts any URL into clean, LLM-ready Markdown, text, or HTML with production-grade features like SSRF protection, rate limiting, retries, caching, and structured error handling.MIT
- AlicenseNot gradedqualityDmaintenanceEnables web scraping and document processing with JavaScript execution, anti-detection measures, batch processing, and structured data extraction. Supports multiple formats including markdown, HTML, screenshots, and handles PDFs with OCR capabilities.4MIT
- AlicenseCqualityBmaintenanceA powerful tool for fetching and extracting text content from web pages and APIs, supporting web scraping, REST API requests, and Google Custom Search integration.510MIT
- AlicenseNot gradedqualityAmaintenanceTurns any URL into structured web intelligence in one call: SEO/OpenGraph metadata, public contacts, tech-stack fingerprinting, Schema.org data, a security-headers audit, and clean AI-ready Markdown. Anti-SSRF hardened by design.10MIT
TDQS
A3.6/5.0
Scored across 2 tools
Disambiguation5/5
The two tools target clearly distinct actions: extract_content pulls raw content from a source, while summarize_content condenses it via an LLM. An agent can easily tell which to call based on whether it needs raw text or a summary.
Naming Consistency5/5
Both names follow the same verb_noun snake_case pattern (extract_content, summarize_content), with no deviations in convention or style.
Tool Count3/5
Two tools is on the thin side for even a focused server, though each earns its place in a natural extract-then-summarize pipeline. A couple of supporting operations (e.g. format conversion or output saving) would round out the surface.
Completeness4/5
The extract and summarize pair covers the core content-processing lifecycle, including multiple input sources (URLs, PDFs, YouTube). Minor gaps exist around batch processing, output formatting, or chunking, but agents can work around these.
Maintenance
ActivityMaintained
ResponsivenessWithin a week