"A server for finding prompts" matching MCP servers:

这是面向智能运维场景的 MCP 工具服务，让 Agent 能够按需检索日志内容并获取主机与应用的监控指标数据。它把实时的日志与指标证据接入 Plan-Execute-Replan 诊断流程，从而支撑自动故障排查与根因分析。

Enables AI agents to manage prompts and sessions via the ai-agent-logs API, with tools for health checks, login, listing/creating prompts, and retrieving sessions.

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

Enables observability and governance for local LLMs via Ollama, including auditing model usage, scanning prompts for secrets/PII, and enforcing allow/deny policies.

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.

MCP server for Langfuse observability. Query traces, debug exceptions, analyze sessions, and manage prompts and datasets for your LLM applications.

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.

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

Enables users to log AI tool activity without conversation text and contribute back only user-approved, small-cell-suppressed counts through a local MCP interface.

A live post-mortem debugger MCP server for Home Assistant that lets AI agents list captured exceptions, walk traceback frames, read local variables, and optionally evaluate Python in the context of a captured frame for true post-mortem debugging.

Enables searching incidents and runbooks, retrieving incident details by URI, and creating postmortem summary prompts through MCP tools, resources, and prompts.

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

Enables web audits for performance, security, SEO, and DNS, returning structured improvement prompts for AI assistants.

Enables read-only querying and analysis of Kibana logs through a logged-in sid Cookie, including keyword/time/index searches, traceId lookups, surrounding log context, and aggregations by service, level, or host. It also validates Kibana connection status and redacts sensitive fields such as cookies, tokens, and passwords.

Enables AI agents to query and analyze Graylog logs through MCP tools for search, aggregation, stream listing, and saved queries, with optional OpenAI-powered natural language orchestration and audit logging.

Enables AI agents to diagnose Linux server incidents by collecting and structuring system diagnostics from multiple servers via SSH, with tools for finding incident clusters, gathering context (memory, CPU, swap, etc.), and running arbitrary commands.

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.

Read-only MCP server for querying AI usage metrics (exact tokens, cost, latency, errors, productivity) from a local SQLite database, enabling charts and analysis in Claude Code and Cursor.

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.

MCP server that lets AI coding agents report task progress and milestones, providing a real-time web dashboard and macOS notifications for monitoring multiple AI workbenches.