genpark-markdown-table-to-json-transformer-skill
Official 1. Click on "Deploy Server".
2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
3. In the chat, type `@` followed by the MCP server name and your instructions, e.g., "`@genpark-markdown-table-to-json-transformer-skill` convert this markdown table to typed JSON: | name | age |
| Ana | 30 |"
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](https://glama.ai/blog/2025-07-08-how-to-install-and-use-mcp-servers).genpark-markdown-table-to-json-transformer-skill
⚡ Overview & Architectural Significance
genpark-markdown-table-to-json-transformer-skill delivers zero-dependency, low-latency web automation, DOM semantic pruning, and execution trajectory evaluation primitives engineered strictly using Python 3.9+ standard library.
🌟 Key Architectural Capabilities
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: agentmd-mcp
🏗️ Architectural Topology & State Machine
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"]🚀 Quickstart & Standalone Execution
Local Python Client Usage
from client import MarkdownTableToJsonTransformer
# Initialize engine
engine = MarkdownTableToJsonTransformer()
# Execute self-testing benchmark suite
result = engine.run_benchmark_table_transformer()
print("Execution Result:", result)🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-markdown-table-to-json-transformer-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-markdown-table-to-json-transformer-skill/mcp_server.py"]
}
}
}📦 Smithery.ai & PyPI Deployment
This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
pip install git+https://github.com/alphaparkinc/genpark-markdown-table-to-json-transformer-skill.gitThis server cannot be deployed
Maintenance
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