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This experimental Model Context Protocol (MCP) server serves as a secure bridge between technically capable Large Language Models (LLMs), such as Claude Sonnet, and the Observe platform. Unlike traditional chatbots, it directly interfaces with Observe APIs to execute OPAL queries, export worksheet data, manage monitors, and retrieve dataset information without involving LLMs in internal functions, thereby preventing private data leakage. The server further enhances interaction by leveraging Pinecone as a vector database for semantic search across OPAL documentation and troubleshooting runbooks, enabling comprehensive troubleshooting and access to vectorized knowledge for in-depth insights.

Características Principales

• Provide context-aware assistance for crafting OPAL queries.
• Manage Observe monitors, including listing and creation.
• Offer semantic search across Observe documentation using a vector database.
• Execute OPAL queries and export Observe worksheet data with flexible time parameters.
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• Recommend relevant troubleshooting runbooks based on user queries.

Casos de Uso

• Enabling large language models (LLMs) to securely interact with and query the Observe platform.
• Streamlining IT operations and troubleshooting by providing LLM-assisted access to runbooks and documentation.
• Automating data retrieval and analysis from Observe for AI-driven insights.