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"Exploring RAG through defined workspaces" matching MCP servers:

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    Enables alarm investigation through MCP tools for asset search, metadata lookup, alarm retrieval, summarization, priority scoring, and operator recommendations, with RAG-backed evidence from procedure documents.
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    Enables querying and exploring Cribl Stream and Edge deployments, providing access to worker groups, fleets, sources, destinations, pipelines, routes, event breakers, and lookups through a structured interface.
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    MIT No Attribution
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    Enables access to Amazon Managed Prometheus workspaces through natural language queries. Supports listing workspaces, executing PromQL queries, and retrieving workspace details and metrics with AWS authentication.
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    An MCP server that enables Large Language Models to retrieve, analyze, and query metric data from Prometheus databases through pre-defined routes.
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    MIT
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    Enables querying Google Analytics 4 data through MCP tools for running reports, listing properties, getting real-time metrics, and exploring metadata.
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    MIT
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    Enables querying and managing Azure Log Analytics workspaces using KQL (Kusto Query Language). Supports executing queries, managing saved queries, and exploring workspace tables and schemas with Service Principal authentication.
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    Enables exploring and diagnosing a Redis instance from MCP clients with read-only safety, using SCAN instead of KEYS for safe key enumeration.
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    MIT
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    Enables AI assistants to securely query and analyze KARTE event data via BigQuery. It provides tools for fetching event logs, aggregating data, and exploring schemas using natural language.
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    Lets your coding agent talk to the RunWhen platform — workspace chat, issues, SLXs, run sessions, and the Tool Builder — over the Model Context Protocol. Enables workspace chat with AI assistant, task authoring via Tool Builder, and direct data access to workspaces, issues, SLXs, run sessions, and more.
    47
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    Apache 2.0
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    Enables data observability operations with the Sifflet platform. Supports exploring assets, monitors, incidents, generating monitor-as-code YAML from descriptions, and performing impact analysis.
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    Enables management and monitoring of farm operations including field and crop tracking, livestock monitoring, equipment management, and sensor readings through a Model Context Protocol interface built with FastMCP.
    11
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    Enables interaction with Datadog APIs through automatically generated tools from Postman collections. Supports monitoring operations, log management, metrics submission, and other Datadog functionality through natural language.
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    Apache 2.0
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    Provides tools for retrieving analytics reports and metrics from PlainSignal through the Model Context Protocol (MCP).
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    MIT