"Searching for information about screen capture or screenshot methods" matching MCP servers:

Provides a local MCP interface to query captured, PII-scrubbed prompt telemetry from coding agents and browser chats, enabling time summaries, prompt search, and activity analysis without data leaving the machine.

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.

Enables AI-driven SOC investigations by providing automated Splunk querying, threat intelligence enrichment, and response actions through natural language. Includes tools for IP pivoting, lateral movement detection, and label harvesting.

A Model Context Protocol server that provides seamless integration with Coroot observability platform, enabling monitoring of applications, analysis of performance metrics, examination of logs and traces, and management of infrastructure through Coroot's comprehensive API.

An MCP server implementation that integrates AI assistants with Langfuse workspaces, allowing models to query LLM metrics by time range.

Provides a standardized way for MCP clients to interact with Apache Airflow's REST API, supporting operations like DAG management and monitoring Airflow system health.

MCP server that integrates with VictoriaMetrics vmanomaly API to enable AI-assisted anomaly detection, model management, and configuration generation.

Enables AI clients like Claude to triage, investigate, and operate Icinga installations through natural language, integrating with Icinga's REST APIs and providing deep awareness of monitoring plugins and historical performance data.

A safety layer and monitoring copilot for AI agents using Splunk MCP, recording tool calls, enforcing policies, and blocking risky actions.

Provides Model Context Protocol access to the Dynatrace platform API, letting AI agents run live queries against observability, security, and platform data and manage a Dynatrace environment. It is hosted remotely by Dynatrace, so MCP hosts can add it by name from the official registry without installing anything.

A remote, Dynatrace-hosted MCP server that gives AI agents live API access to your Dynatrace environment. It enables querying and analyzing observability, security, and platform data — such as DQL queries, logs, traces, problems, and dashboards — so agents can act on production context instead of static knowledge.

Read-only MCP server for searching and aggregating logs from VMware Aria Operations for Logs (formerly vRealize Log Insight). Enables querying log events, aggregating with spike detection, and retrieving alert information.

MCP server for reading, filtering, and analyzing NVIDIA Nsight Graphics captures via ngfx-replay, exposing tools for capture inspection, GPU Trace profiling, and replay analysis.

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Enables querying macOS Screen Time data to retrieve app usage, hourly breakdown, and custom SQL queries through MCP.

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.

Enables LLMs to directly query a Matomo analytics instance, execute reporting methods, fetch historical trends, report metadata, and dynamically generated chart images through typed MCP tools.

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.

Enables AI assistants to interact with tshark and related command-line tools for live packet capture, PCAP analysis, filtering, stream following, and capture file merging.

Provides Claude with access to Shodan for IP address lookups, service discovery, and vulnerability scanning, allowing users to query information about hosts, count results, and check API usage.

Enables users to ask about render performance in React and React Native apps via natural language, exposing tools for listing render hotspots and explaining component re-renders.

Read-only MCP server that analyzes saved frame buffers, traces, and capture receipts to pinpoint the earliest provable divergence and plan the smallest next capture for AI-assisted game debugging.

Provides read-only access to query and retrieve information about devices, fleets, events, and configurations managed by Flight Control through a safe integration layer supporting filtering and selector-based queries.

Detrix is an agentic debugger. Watch any variable at any line, zero code changes during debugging Local or cloud - same workflow for Docker containers and remote hosts Python, Go, Rust - observation points capture values without pausing, without restarting

Enables remote log searching, tailing, and file reading across servers via MCP tools, with pluggable execution via SSH, pyinfra, or local commands.

Enables local analysis of classic libpcap capture files through MCP tools covering DNS, DHCP, ICMP, TCP connections, anomalies, retransmissions, traffic flows, SIP calls, and capture summaries without network access or API keys.

An MCP server for Arkime full packet capture that enables AI assistants to search network sessions, investigate traffic patterns, and monitor capture health through various network analysis tools.

Provides secure, multi-user access to Oracle Database for MCP clients, enabling SQL querying, execution, schema exploration, and performance analysis through various connection methods including TNS, JDBC, and TCPS.

Enables agents to detect drift anomalies with CUSUM, forecast trends using Holt linear methods, and automatically trip a circuit breaker to protect workflows.

Enables integration with Datadog APIs to monitor and retrieve information about monitors, metrics, dashboards, logs, events, and incidents through the Model Context Protocol.

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.

Provides tools to search Splunk logs, inject test data, and send triage emails, enabling AI-driven incident investigation.

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.

Enables coding agents to run explicit local analysis and capture operations, turning artifacts or commands into bounded runtime evidence with provenance, coverage, limitations, and optional durable preservation. It coordinates profilers, benchmark tools, trace processors, and direct targets for investigating hotspots in application/native code, GPU kernels, and inference stacks.

Enables coding agents to gather bounded runtime evidence by coordinating profilers, benchmark tools, and trace processors against explicit artifacts or commands. Exposes analysis and capture tools through an unbound workspace, returning provenance, coverage, and limitations with optional durable preservation.

Enables coding agents to run bounded local profiling, tracing, benchmarking, and capture operations from explicit artifacts or commands, then inspect typed evidence, provenance, coverage, limitations, and preserved replay sources through MCP tools.

Enables coding agents to coordinate profilers, benchmark tools, and trace processors to turn explicit artifacts or commands into bounded runtime evidence with typed provenance, coverage, and limitations. Provides named analysis and capture tools for tracing and profiling hotspots across application code, native code, GPU kernels, and inference stacks.

Enables coding agents to run bounded analysis and capture operations against explicit artifacts or commands, returning typed evidence, provenance, coverage, limitations, and optional preserved evidence through MCP tools.