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    The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.
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    MIT
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    A Model Context Protocol server that provides AI agents with controlled read access to Datalust Seq instances for log analysis and monitoring. It enables agents to search events, execute data queries, and retrieve information about signals, dashboards, and alerts.
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    Enables LLMs to analyze and manage DBOS workflows, including introspection, workflow management, and authentication, to help debug applications in development or production.
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    MIT
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    Enables integration with Datadog APIs to monitor and retrieve information about monitors, metrics, dashboards, logs, events, and incidents through the Model Context Protocol.
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    MIT
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    Enables AI assistants to query and fetch error and performance monitoring data from AppSignal through the Model Context Protocol. Supports searching and retrieving detailed information about application errors and performance samples with flexible filtering options.
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    MIT
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    Enables comprehensive system monitoring and diagnostics through 18 tools that provide detailed information about CPU, memory, disk usage, network interfaces, running processes, battery status, hardware details, and temperature monitoring. Allows users to query system information and performance metrics through natural language interactions.
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    ISC
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    Enables AI assistants to interact with Splunk Enterprise and Splunk Cloud instances through standardized MCP interface. Supports executing SPL queries, managing indexes and saved searches, listing applications, and retrieving server information with flexible authentication options.
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    An MCP server that connects to Sentry.io or self-hosted Sentry instances to retrieve and analyze error reports, stack traces, and debugging information.
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    MCP server exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.
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    Provides Claude with read-only access to PC diagnostics including hardware specs, live performance metrics, disk usage, network checks, and more, enabling natural language queries about PC health and performance.
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    MIT
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    MCP server for Prometheus metrics and observability. Give Claude (or any MCP-capable agent) read access to your Prometheus instance — query metrics with PromQL, inspect active alerts, and explore scrape targets without leaving the conversation. Tools: prometheus_list_metrics, prometheus_query, prometheus_query_range, prometheus_list_alerts, prometheus_list_targets.
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    MIT
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    Enables AI assistants to query and analyze Alibaba Cloud SLS logs using natural language, supporting multiple log sources like Function Compute and ECS. It provides tools for searching logs, performing SQL analysis, and visualizing log distributions directly within Cursor or Claude.
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    MIT