biohackathon2025MCP
Verified Safeby cp-weiland
Overview
This project demonstrates an AI agent interacting with multiple local Micro-Co-Pilot (MCP) servers, each providing specialized tools for tasks like hashing, weather forecasting, and SPARQL queries against various data sources.
Installation
python simpleClient.pySecurity Notes
The system directly executes LLM-generated SPARQL queries against public endpoints (DBpedia, Research Vocabularies Australia). While `SPARQLWrapper` handles the protocol, an LLM could formulate resource-intensive or unintended queries. The `simpleServerFLOPO.py` mitigates this by hardcoding the query in its description. No `eval`, obfuscation, or hardcoded secrets were identified. Network risks are confined to calls to known public APIs (weather.gov, various SPARQL endpoints).
Similar Servers
mcp-Server
Provides a framework for building and integrating AI agent tools, demonstrating how a client can orchestrate multiple MCP servers (math and weather) via a language model.
MCP-AGENT
Develop, automate, and integrate AI agents by connecting them to external tools and Model Context Protocol (MCP) servers for multi-step workflows and task completion.
mcp_server_weather_jayden
This server provides current and forecasted weather data for specific geographical coordinates as a tool callable by an AI agent.
mcp-server-orchestrator
An orchestration system for AI agents to interact with custom tools via the Model Context Protocol (MCP), integrating Large Language Models (LLMs) like OpenAI with backend services.