corbinvachal48/agentrender-mcp
by Various
MCP + REST: URL to screenshot, PDF, or structured extract for AI agents
MCP
corbinvachal48/agentrender-mcp
Added 23 Sept 2026
Overview
agentrender-mcp is a Python-based Model Context Protocol (MCP) server that converts URLs into screenshots, PDFs, or structured data extractions. It exposes both MCP and REST interfaces, allowing AI agents to request web page rendering and content extraction as part of their workflows.
Best for
Best for
AI agent developers needing simple URL-to-render and extraction capabilities via MCP
Use cases
- Generate screenshots of web pages for agent visual context
- Create PDFs from URLs for document processing
- Extract structured data from web pages for agent analysis
How to use
Install
pip install agentrender-mcp Tools exposed
AGENTRENDER_API_KEYAGENTRENDER_BASE_URL
Tested with
Claude Desktop, Cursor
Notes
agentrender-mcp is a Python-based Model Context Protocol (MCP) server that converts URLs into screenshots, PDFs, or structured data extractions. It exposes both MCP and REST interfaces, allowing AI agents to request web page rendering and content extraction as part of their workflows.
0 stars on GitHub. Last updated 2026-09-09. Licensed MIT.
Use cases
- Generate screenshots of web pages for agent visual context
- Create PDFs from URLs for document processing
- Extract structured data from web pages for agent analysis
Pros
- Supports both MCP and REST, offering flexible integration
- Written in Python, easy to extend and deploy
- Covers rendering, PDF, and extraction in one tool
Cons
- Early-stage project with no stars or community adoption
- Limited documentation or examples may be sparse
- Single-purpose tool, may overlap with existing services
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Supports both MCP and REST, offering flexible integration
- Written in Python, easy to extend and deploy
- Covers rendering, PDF, and extraction in one tool
Cons
- Early-stage project with no stars or community adoption
- Limited documentation or examples may be sparse
- Single-purpose tool, may overlap with existing services
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