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GenPark Trie Prefix Keyword Matcher

GenPark Trie Prefix Keyword Matcher

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genpark.aiAI & Agents7Updated 6d ago

About GenPark Trie Prefix Keyword Matcher

GenPark Trie Prefix Keyword Matcher is a Python MCP server and library for in-memory Trie prefix matching and multi-keyword search. It is designed for prompt routing and entity extraction.

Key features

  1. 01In-memory Trie prefix matching
  2. 02Multi-keyword search
  3. 03Prompt routing support
  4. 04Entity extraction support
  5. 05Python standard library only

Use cases

  1. 01Route prompts by matching terms
  2. 02Extract entities with keyword searches

MCP support

It runs as an MCP server for use by MCP clients. The README also describes direct use as a Python library.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
mcpbgenpark-trie-prefix-keyword-matcher-skill-v1.0.1.mcpb

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

GenPark Trie Prefix Keyword Matcher

License: MIT Python Standard Library Model Context Protocol GenPark

High-performance in-memory Trie and multi-keyword search engine for prompt routing and entity extraction.

Part of the GenPark AI Agent Skills Ecosystem.


🌟 Key Features

  • 100% Python Standard Library: Zero third-party runtime dependencies.
  • MCP Native: Plugs directly into Claude Desktop, Cursor, LobeChat, and autonomous agent DAGs.
  • Production Resilience: Strict schema conformance, zero data loss, enterprise hardened.

🚀 Installation & Usage

Run as an MCP server:

python mcp_server.py

Use directly as a library:

from client import *

See example_usage.py for comprehensive integration demonstrations.

📄 License

MIT License. Maintained by GenPark AI.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.