Content Core MCP Server

Content Core is an AI‑powered content extraction and processing platform that turns any source—web pages, documents, media files, or images—into clean, structured text ready for downstream analysis. By abstracting the complexities of format‑specific parsers and AI summarization, it lets developers focus on higher‑level logic while the server handles data ingestion, intelligent engine selection, and optional LLM‑based refinement.

The core problem Content Core solves is the fragmented nature of content ingestion. Traditional workflows require separate tools for PDFs, Word files, web scraping, and media transcription, each with its own API or command‑line interface. Content Core unifies these into a single MCP server exposing resources, tools, and prompts that can be invoked from any AI assistant. This reduces friction for developers who need to incorporate diverse content into conversational agents, knowledge bases, or analytics pipelines.

Key capabilities include:

• Intelligent Auto‑Detection that inspects a file or URL and chooses the optimal extraction path (e.g., Firecrawl for web pages, Docling for PDFs, Whisper for audio).
• AI‑Powered Cleaning and Summarization where extracted text is passed through LLMs to format, remove noise, or generate summaries in styles such as bullet points, executive briefs, or child‑friendly explanations.
• Asynchronous, Zero‑Install Access via , enabling instant CLI commands without prior installation.
• Rich Integration Options: a Python library, Raycast extension, macOS Finder services, and direct MCP usage in Claude or other assistants.

Real‑world use cases span from building knowledge‑base bots that pull information from corporate intranets, to automating content moderation pipelines that ingest videos and produce transcriptions for compliance checks. A marketing team can quickly summarize long PDF reports into concise briefs, while a data scientist might extract structured tables from scanned documents for analysis.

In AI workflows, Content Core plugs into the data‑collection stage. An assistant can request or tools, receive a structured JSON payload, and then feed that into downstream models for question answering, sentiment analysis, or report generation. Its unique advantage lies in the seamless blend of rule‑based extraction and LLM refinement, ensuring high quality output across heterogeneous media while keeping the integration surface minimal.