"An exploration of the term 'multimodal' and its applications" matching MCP servers:

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

MITM proxy MCP server that intercepts and verifies AI agent tool calls, detecting fabrication, tracking costs, verifying outcomes, and inferring user satisfaction.

A verifying MCP gateway that pins approved tool manifests and skill files to content addresses, blocking tampered descriptions before they reach model context.

Provides MCP tools to create and query SigNoz trace funnels, enabling agents to measure their own step conversion rates from within the agent loop.

Enables natural language interaction with Plausible Analytics data to query traffic, visitors, engagement, and more using conversational questions.

A production-oriented reliability, benchmarking, and evaluation system that layers multi-version protocol negotiation, adaptive streaming, connection pooling, and resilience onto an MCP agent runtime.

Read-only MCP server that surfaces UniFi network data from a UCG-Fiber console to Claude, enabling monitoring of hosts, sites, devices, clients, ISP metrics, and device stats via read-only API tools.

A Model Context Protocol (MCP) server that lets an AI assistant read and operate the Lumics network monitoring platform via its REST API.

An MCP server that lets an agent read its own eval results, run suites, explain failures, check drift, and view judge calibration status.

Enables read-only querying of Azure Log Analytics and Azure Resource Graph through MCP, supporting KQL queries, workspace discovery, and resource inventory exploration with Azure RBAC authentication.

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.

Ships only the MCP server, not the recorder. Recording works by wrapping a process on the host — a local proxy, a PATH shim, a shadow git index. None of that survives a container boundary, so orca record inside here would watch an empty namespace. What does travel is the read side: the six MCP tools serve runs that already exist on disk, so a client mounts its .orca directory and r

Exposes Spark History Server data as tools for AI agents, enabling natural language querying of Spark applications, jobs, stages, and performance metrics.

A read-only Windows MCP server that gives AI assistants awareness of the current PC state, including open applications, focus, idle status, and system metrics.

A stdio MCP server that connects directly to a Scouter Collector over TCP and queries XLogs, counters, and objects. Its purpose is to let an AI quickly explore Scouter metrics and diagnose root causes.

A read-only MCP server that exposes Azure incident-investigation tools for a single App Service and its Application Insights telemetry, enabling an agent to correlate deployment history, platform metrics, and transaction traces.

The MCP server provides an interface to the Datadog API, enabling seamless management of incidents, monitoring, logs, dashboards, metrics, traces, and hosts. Its extensible design allows easy integration of additional Datadog APIs for future expansions.

Enables an LLM client to scan Windows event log files (EVTX) for suspicious activity using Hayabusa, and browse its detection rules directly in conversation.

Exposes Spark History Server metrics and metadata as tools for LLM-based analysis of Spark applications. It enables deep optimization of Spark jobs by providing access to job summaries, stage details, SQL execution plans, and executor performance.