Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@genpark-html-dom-semantic-tree-pruner-skill Prune this raw HTML into a semantic tree and extract the table as JSON"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-html-dom-semantic-tree-pruner-skill delivers zero-dependency, low-latency web automation, DOM semantic pruning, and execution trajectory evaluation primitives engineered strictly using Python 3.9+ standard library.
Zero External Dependencies: Operates exclusively via pure Python (html.parser, urllib.parse, re, math, json). Zero pip install overhead, zero headless browser crashes.
Enterprise Web Agent Invariants: Implements formal token-pruning algorithms, form auto-mapping, anti-crawler trap normalization, Markdown-to-JSON type inference, and trajectory Levenshtein distance evaluation.
Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
Related MCP server: genpark-web-form-input-schema-auto-mapper-skill
flowchart TD
RawWeb["Raw Web Page / DOM Ingress"] --> TrapFilter["URL Canonicalization & Anti-Crawler Trap Guard"]
TrapFilter --> DOMPruner["HTML DOM Semantic Tree Pruner
(80%+ Token Reduction, Strips Scripts/Styles/SVG)"]
DOMPruner --> FormMapper["Web Form Input Schema Auto-Mapper
(Attribute & Heuristic Profile Field Binding)"]
DOMPruner --> TableParser["Markdown & HTML Table to JSON Transformer
(Type-Inferred Structured Record Generation)"]
FormMapper --> AgentExecution["Autonomous Agent Browser Interaction"]
TableParser --> AgentExecution
AgentExecution --> TrajectoryEval["Synthetic Trajectory Evaluator
(Action Precision, Recall & Levenshtein Edit Distance)"]
TrajectoryEval --> VerifiedTaskDone["Verified Benchmark Task Completion"]from client import HtmlDomSemanticTreePruner
# Initialize engine
engine = HtmlDomSemanticTreePruner()
# Execute self-testing benchmark suite
result = engine.run_benchmark_dom_pruner()
print("Execution Result:", result)Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-html-dom-semantic-tree-pruner-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-html-dom-semantic-tree-pruner-skill/mcp_server.py"]
}
}
}This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
pip install git+https://github.com/alphaparkinc/genpark-html-dom-semantic-tree-pruner-skill.gitThis server cannot be deployed
Deterministic web intake and data utilities for autonomous agents.
Clean Markdown and AI-readability scoring for any URL. Built for AI agents.
Web scraping for AI agents. Converts URLs to clean, LLM-ready Markdown with anti-bot bypass.
MCP server (stdio): fetch web pages as clean readable markdown via the AgentForge API
