Agentic RAG HyDE Query Expander
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., "@Agentic RAG HyDE Query ExpanderExpand the query 'postgres deadlocks' into 3 hypothetical passages for vector search"
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-agentic-rag-hyde-hypothetical-document-generator-skill
Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)
Agentic RAG HyDE (Hypothetical Document Embeddings) Query Expander. Bridges dense lexical and semantic gaps by generating diverse hypothetical answer passages from sparse user queries to dramatically boost vector search recall.
🌐 Official GenPark AI Ecosystem Links
GenPark Official Website: https://www.genpark.ai
LinkedIn Official Page: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)
Related MCP server: DocAgent-MCP
🌟 Key Features
100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0
initialize,tools/list, andtools/call.Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
Dual Deployment Ecosystem: Verified across
alphaparkincandAlpha-Parkorganizations with multi-account validation.
🚀 Quick Start
1. Direct Python SDK Usage
from client import AgenticRAGHyDEQueryExpander
def main():
expander = AgenticRAGHyDEQueryExpander()
query = "how to prevent postgresql deadlocks on concurrent bulk updates"
res = expander.generate_hypothetical_passages(query, domain="database", perspectives_count=3)
print("--- HyDE Hypothetical Expansion ---")
print(f"Original Query: {res['original_query']}")
for p in res['passages']:
print(f"[{p['perspective_id']}] (Weight: {p['dense_retrieval_weight']}): {p['hypothetical_passage'][:100]}...")
if __name__ == "__main__":
main()
2. Run as Model Context Protocol (MCP) Server
Start standard JSON-RPC 2.0 server over stdio:
python mcp_server.pyExecute embedded test harness:
python mcp_server.py --test🛠️ MCP Tool Specification
Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.
📜 License & Community
Licensed under the MIT License. Copyright © 2026 GenPark AI. Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550
This server cannot be deployed
Maintenance
Related MCP Connectors
AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.
Text generation over MCP: prose, emails, blog outlines, SQL, humanizing, text diffs, fake data.
- WauldoOAuthcom.wauldo
Stateless agentic tools over MCP: concept extraction, long-context, knowledge graph, planning.
Generate contextual prompts and reusable agent skills, evaluate prompts with the 16-dimension Prompt Score, and manage saved work in PromptDrive. Twelve MCP tools also provide authorized access to private Memory for source-grounded answers. Connect over Streamable HTTP using OAuth 2.1 and PKCE. Generation consumes account quota and automatically saves successful results; Memory access follows account permissions and plan limits.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceExposes RAG and document intelligence pipelines as 8 composable tools for MCP-compatible clients, enabling querying, indexing, classifying, extracting, and assessing documents.1MIT
- FlicenseNot gradedqualityDmaintenanceEnables local document question-answering and retrieval via MCP, supporting multi-turn conversation, intent recognition, and tools for document search, Q&A, and summarization.5-
- AlicenseNot gradedqualityCmaintenanceEnables MCP clients to query a modular RAG knowledge hub with hybrid dense/sparse retrieval, reranking, multimodal image support, and traceable observability, all through natural language tools.MIT
- AlicenseNot gradedqualityBmaintenanceEnables querying a modular RAG knowledge base via MCP, with hybrid retrieval, reranking, and document summary tools.MIT