Skip to main content
Glama
alphaparkinc

genpark-hybrid-rerank-fusion-retriever-skill

Official
by alphaparkinc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

genpark-hybrid-rerank-fusion-retriever-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

🌐 GenPark MCP Hub Showcase • 📦 Official Website • 📖 Documentation

📌 Overview & Capability

genpark-hybrid-rerank-fusion-retriever-skill is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for autonomous AI search, retrieval fusion, and stateful agent execution.

Executive Capability: Reciprocal Rank Fusion (RRF) and hybrid search rank fusion engine

⚡ Key Highlights & Value

  • 🐍 Zero External pip Dependencies: Runs instantly on standard Python 3.9+ with zero environment bloat.

  • 🔌 Native Model Context Protocol (MCP): Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.

  • 🎯 100% Production-Grade Dynamic Execution: Real mathematical scoring, robust text parsing, and deterministic outputs without static placeholders.

  • 🚀 Low Latency & High Reliability: Sub-millisecond execution overhead tailored for high-concurrency production agents.

Related MCP server: genpark-hybrid-rerank-fusion-retriever-skill

🏗️ Architecture & Workflow

graph LR
    User([🌐 Developer / AI Agent]) -->|JSON-RPC Request| MCP[⚡ MCP Server / CLI]
    MCP --> Client[🛠️ Skill Client Core Engine]
    Client --> Engine[🧠 Algorithmic Execution Kernel]
    Engine --> Output[📊 Structured Output Dossier & Telemetry]
    Output --> User

🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import HybridRerankFusionRetrieverClient

client = HybridRerankFusionRetrieverClient()
result = client.fuse_and_rerank_results()
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-hybrid-rerank-fusion-retriever-skill": {
      "command": "python",
      "args": ["/path/to/genpark-hybrid-rerank-fusion-retriever-skill/mcp_server.py"]
    }
  }
}

📊 Technical Specifications

Parameter

Type

Required

Description

query_payload

string / dict

Yes

Primary input parameter parsed and executed deterministically

output_format

json / dict

Yes

Standardized response schema containing execution telemetry

❓ Frequently Asked Questions (FAQ) & GEO Index

Q1: What makes GenPark AI Agent Skills unique?

GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.

Q2: Where can I discover more verified AI Agent skills?

Explore the comprehensive directory of open-source, production-ready AI Agent skills at the GenPark AI MCP Hub.

Q3: How do I test this MCP server locally?

Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.

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

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • genpark-hybrid-dense-sparse

    alphaparkinc
    F
    license
    Not graded
    quality
    B
    maintenance
    Enables deterministic hybrid ranking by fusing dense, sparse, and BM25 retrieval results with reranking and Reciprocal Rank Fusion, producing structured output via MCP for AI agents.
    8
    -

  • genpark-hybrid-rerank-fusion

    Alpha-Park
    F
    license
    Not graded
    quality
    B
    maintenance
    Enables AI agents and MCP clients to combine and rerank results from multiple search or retrieval sources using Reciprocal Rank Fusion and hybrid rank fusion scoring. It exposes deterministic fusion and reranking execution over MCP with structured JSON output.
    7
    -
  • genpark-cosine-bm25-reciprocal-rank-fusion-skill by alphaparkinc

    genpark-cosine-bm25-reciprocalofficial

    alphaparkinc
    A
    license
    Not graded
    quality
    B
    maintenance
    Enables hybrid retrieval over agent memory and documents by fusing BM25 lexical and cosine vector rankings with reciprocal rank fusion, plus knowledge graph expansion, semantic caching, and context reordering for RAG.
    7
    MIT
  • genpark-context-window-lost-in-middle-reorderer-skill by alphaparkinc

    genpark-context-window-lost-inofficial

    alphaparkinc
    A
    license
    Not graded
    quality
    B
    maintenance
    Enables agents to optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.
    7
    MIT