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Exposes Content Core extraction, cleaning, and summarization as an MCP server for Claude Desktop and other MCP clients.
Configuration
View docs{
"mcpServers": {
"lfnovo-content-core": {
"command": "uvx",
"args": [
"--from",
"content-core",
"content-core-mcp"
]
}
}
}Content Core provides an MCP server that lets you expose its extraction, cleaning, and summarization capabilities to MCP-compatible clients like Claude Desktop. This server enables seamless, programmatic access to Content Coreβs AI-powered content processing through a standardized interface, so you can automate content workflows from your preferred MCP-enabled applications.
To use the Content Core MCP server, run the MCP endpoint locally and point your MCP client to it. The following setup shows how to enable the server and wire it to Claude Desktop. You can also connect from other MCP-compatible clients using the same pattern.
Prerequisites: you need Python and a way to run MCP-compatible commands. You also need the uv tool for isolated execution.
1) Install Content Core and its MCP component. You can install Content Core in your Python environment. You do not need system libraries for the core installation.
2) Start the MCP server using the provided runtime command. The MCP server is exposed via a local command you run with uv to enable zero-install execution.
3) Add the MCP server configuration to your Claude Desktop setup so it can discover and communicate with the Content Core MCP server.
4) Validate connectivity by triggering an extraction or summarization from Claude Desktop and confirming the results flow back correctly.
{
"mcpServers": {
"contentcore_mcp": {
"command": "uvx",
"args": ["--from", "content-core", "content-core-mcp"]
}
}
}Add the MCP server entry to Claude Desktopβs configuration so it can connect automatically. Use the following JSON snippet as a reference.
{
"mcpServers": {
"contentcore_mcp": {
"command": "uvx",
"args": ["--from", "content-core", "content-core-mcp"]
}
}
}Tool to trigger content extraction from text, URLs, or files via Content Core and return structured results.
Tool to clean and normalize extracted content, removing noise and unnecessary formatting.
Tool to generate AI-powered summaries with optional context for different styles.