Skip to main content
Glama
Sign In
genpark-agent-self-consistency-consensus-scorer-skill

genpark-agent-self-consistency-consensus-scorer-skill

Official
by alphaparkinc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

genpark-agent-self-consistency-consensus-scorer-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation

📌 Overview & Capability

genpark-agent-self-consistency-consensus-scorer-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server designed for autonomous AI agents, multi-agent frameworks (LangGraph, CrewAI, AutoGen, OpenAI Swarm), and developer environments (Cursor, Windsurf, Claude Desktop).

Executive Capability: Multi-path chain-of-thought self-consistency consensus scorer and majority voter aggregating divergent LLM reasoning trajectories into high-confidence deterministic outputs.

⚡ Key Highlights

  • 🐍 Zero External pip Dependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.

  • 🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.

  • ⚡ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.

  • 🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.

Related MCP server: genpark-agent-self-consistency-consensus-scorer-skill

🏗️ Architecture

graph LR
    Agent([🤖 Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[⚡ genpark-agent-self-consistency-consensus-scorer-skill Server]
    Server --> Core[🧠 Deterministic Processing Core]
    Core --> Out[📊 Actionable Result & Telemetry]
    Out --> Agent

🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import AgentSelfConsistencyConsensusScorer

client = AgentSelfConsistencyConsensusScorer()
result = client.run_consensus_benchmark()
print(result)

🔌 Model Context Protocol (MCP) Setup

Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:

claude_desktop_config.json

{
  "mcpServers": {
    "genpark-agent-self-consistency-consensus-scorer-skill": {
      "command": "python",
      "args": ["/path/to/genpark-agent-self-consistency-consensus-scorer-skill/mcp_server.py"]
    }
  }
}

Direct MCP Testing

python mcp_server.py --test

📊 Technical Specifications

Parameter

Type

Required

Description

payload

string / dict

Yes

Primary context, code, schema, or content input

options

dict

No

Execution flags, compression ratios, or risk bounds

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Resources

Related MCP Connectors

Related MCP Servers

  • genpark-multi-agent-debate

    Alpha-Park
    F
    license
    Not graded
    quality
    Not graded
    maintenance
    Enables multi-agent debate cross-examination and consensus arbitration through a deterministic, zero-dependency MCP server, providing structured outputs and telemetry for autonomous AI workflows.
    -

  • genpark-agent-self-consistency

    Alpha-Park
    F
    license
    Not graded
    quality
    B
    maintenance
    Aggregates multiple divergent LLM reasoning trajectories through majority voting and consensus scoring to produce high-confidence deterministic outputs without additional model calls. Plug into any MCP-compliant client to resolve agent tasks with sub-millisecond, zero-dependency execution.
    7
    -
  • genpark-multi-agent-debate-consensus-synthesizer-skill

    genpark-multi-agent-debateofficial

    alphaparkinc
    A
    license
    Not graded
    quality
    B
    maintenance
    Enables AI agents to run structured multi-persona debates over high-stakes decisions and merge their positions into an explainable consensus, complete with logged dissenting minority opinions. It runs as a zero-dependency Python MCP server or importable module, compatible with Claude Desktop, Cursor, and custom agent frameworks.
    7
    MIT

  • genpark-reasoning-budget-test

    Alpha-Park
    A
    license
    Not graded
    quality
    Not graded
    maintenance
    Enables agents to dynamically allocate test-time compute, including entropy-driven thinking token budgets, temperature annealing, and reflection passes, while verifying reasoning via MCTS search with process reward modeling, counterfactual backtracking, formal proof checking, and majority-vote consensus. Runs as a zero-dependency Python MCP server over STDIO JSON-RPC for integration with Claude Desktop, Cursor, or custom agent runtimes.
    7
    MIT