mcp server
genpark-topological-sorter-tarjan-scc-skill
Directed acyclic graph (DAG) topological sorting and Tarjan's strongly connected components (SCC) engine for agent dependency resolution.
Set up
Runs on your computer. Pick your agent and operating system.
This project does not publish an install method we can turn into a config. Follow the README instead.
README
synced Oct 21, 2026genpark-topological-sorter-tarjan-scc-skill
⚡ Overview & Architectural Significance
genpark-topological-sorter-tarjan-scc-skill delivers zero-dependency graph pathfinding, topological dependency resolution, network maximum flow, and centrality ranking engineered strictly using Python 3.9+ standard library.
🌟 Key Architectural Capabilities
- Zero External Dependencies: Operates exclusively via pure Python (
heapq,collections,math,json). Zero NetworkX or SciPy build overhead. - Enterprise Graph Invariants: Implements formal Dijkstra/A* priority queue path traversal, Kahn's DAG topological sorting, Edmonds-Karp BFS residual flow augmentation, Kruskal's disjoint-set minimum spanning tree, and PageRank random surfer power iteration.
- Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
🏗️ Architectural Topology & State Machine
flowchart TD
GraphInput["Graph Topology: Nodes & Weighted Edges"] --> AlgorithmRouter["Graph & Network Routing Kernel"]
AlgorithmRouter --> Pathfinder["Dijkstra & A* Shortest Pathfinder"]
AlgorithmRouter --> DAGAnalyzer["Topological Sorter & Dependency Resolver"]
AlgorithmRouter --> FlowSolver["Edmonds-Karp Maximum Flow Solver"]
AlgorithmRouter --> MSTBuilder["Kruskal's Minimum Spanning Tree"]
AlgorithmRouter --> CentralityEngine["PageRank Authority & Centrality"]
Pathfinder --> ExecutionPlan["Optimal Multi-Agent Execution Plan"]
DAGAnalyzer --> ExecutionPlan
FlowSolver --> ExecutionPlan
MSTBuilder --> ExecutionPlan
CentralityEngine --> ExecutionPlan
🚀 Quickstart & Standalone Execution
Local Python Client Usage
from client import GraphDAGAnalyzer
# Initialize engine
engine = GraphDAGAnalyzer()
# Execute self-testing benchmark suite
result = engine.benchmark_topological_analysis()
print("Execution Result:", result)
🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-topological-sorter-tarjan-scc-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-topological-sorter-tarjan-scc-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-topological-sorter-tarjan-scc-skill.git