lmcc-dev/mult-fetch-mcp-server
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This project implements an MCP-compliant client and server for communication between AI assistants and external tools.
Tools
Functions exposed to the LLM to take actions
fetch_html
Fetch a website and return the content as HTML. Best practices: 1) Always set startCursor=0 for initial requests, and use the fetchedBytes value from previous response for subsequent requests to ensure content continuity. 2) Set contentSizeLimit between 20000-50000 for large pages. 3) When handling large content, use the chunking system by following the startCursor instructions in the system notes rather than increasing contentSizeLimit. 4) If content retrieval fails, you can retry using the same chunkId and startCursor, or adjust startCursor as needed but you must handle any resulting data duplication or gaps yourself. 5) Always explain to users when content is chunked and ask if they want to continue retrieving subsequent parts.
fetch_json
Fetch a JSON file from a URL. Best practices: 1) Always set startCursor=0 for initial requests, and use the fetchedBytes value from previous response for subsequent requests to ensure content continuity. 2) Set contentSizeLimit between 20000-50000 for large files. 3) When handling large content, use the chunking system by following the startCursor instructions in the system notes rather than increasing contentSizeLimit. 4) If content retrieval fails, you can retry using the same chunkId and startCursor, or adjust startCursor as needed but you must handle any resulting data duplication or gaps yourself. 5) Always explain to users when content is chunked and ask if they want to continue retrieving subsequent parts.
fetch_txt
Fetch a website, return the content as plain text (no HTML). Best practices: 1) Always set startCursor=0 for initial requests, and use the fetchedBytes value from previous response for subsequent requests to ensure content continuity. 2) Set contentSizeLimit between 20000-50000 for large pages. 3) When handling large content, use the chunking system by following the startCursor instructions in the system notes rather than increasing contentSizeLimit. 4) If content retrieval fails, you can retry using the same chunkId and startCursor, or adjust startCursor as needed but you must handle any resulting data duplication or gaps yourself. 5) Always explain to users when content is chunked and ask if they want to continue retrieving subsequent parts.
fetch_markdown
Fetch a website and return the content as Markdown. Best practices: 1) Always set startCursor=0 for initial requests, and use the fetchedBytes value from previous response for subsequent requests to ensure content continuity. 2) Set contentSizeLimit between 20000-50000 for large pages. 3) When handling large content, use the chunking system by following the startCursor instructions in the system notes rather than increasing contentSizeLimit. 4) If content retrieval fails, you can retry using the same chunkId and startCursor, or adjust startCursor as needed but you must handle any resulting data duplication or gaps yourself. 5) Always explain to users when content is chunked and ask if they want to continue retrieving subsequent parts.
fetch_plaintext
Fetch a website and return the content as plain text with HTML tags removed. Best practices: 1) Always set startCursor=0 for initial requests, and use the fetchedBytes value from previous response for subsequent requests to ensure content continuity. 2) Set contentSizeLimit between 20000-50000 for large pages. 3) When handling large content, use the chunking system by following the startCursor instructions in the system notes rather than increasing contentSizeLimit. 4) If content retrieval fails, you can retry using the same chunkId and startCursor, or adjust startCursor as needed but you must handle any resulting data duplication or gaps yourself. 5) Always explain to users when content is chunked and ask if they want to continue retrieving subsequent parts.
Prompts
Interactive templates invoked by user choice
mult-fetch-mcp:prompt:fetch-website
Fetch website content
mult-fetch-mcp:prompt:extract-content
Extract specific content from a website
mult-fetch-mcp:prompt:debug-fetch
Debug website fetching issues
Resources
Contextual data attached and managed by the client
mult-fetch-mcp:log:debug-log
URI: file:///logs/debug
MIME: null
Debug log file containing all debug messages from the server
mult-fetch-mcp:log:clear-debug-log
URI: file:///logs/clear
MIME: null
Clear the debug log file
