"How to export LLM chat history from multiple domains" matching MCP servers:

Enables LLMs to read and analyze Spark History Server data, including jobs, stages, executors, and SQL executions, to diagnose failures and optimize performance.

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

MCP server for tracking LLM costs, setting spending limits, and receiving budget alerts directly within AI editors via slash commands like /guard.

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.

Connect AI agents like Claude, Cursor, and Gemini to historical Windows performance and privacy data gathered by the AppControl Windows app. This server enables natural language queries into CPU, GPU, and temperature history alongside system security events for deep local auditing.

Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.

A MCP server for tracking AI usage metrics and structured logs across applications. Monitor model calls, analyze usage patterns, track costs, and debug AI interactions.

Unofficial MCP server for Vexo analytics. Enables querying event names, counts, timelines, sessions, and recent events from Vexo Export API through natural language.

Enables LLM agents to query real-time and historical analytics from WireBoard, allowing natural language questions about visitors, events, and site metrics.

Enables querying logs, traces, and metrics from multiple OpenObserve instances via MCP tools, with parallel execution, batching, and caching.

An MCP server for debugging Temporal workflows, enabling LLM-assisted inspection of workflow executions, detailed workflow info, and event history.

Enables MCP-capable AI clients to search ClickHouse logs, browse instances, databases, and tables, and inspect log histograms directly from a chat.

Enables AI assistants to manage Stape.io sGTM containers, CAPI Gateways, custom domains, and power-ups via 10 tools.

Enables LLM clients to ask natural-language questions about nodes in a FABRIC slice, returning grounded answers from the Node Exporter Full dashboard's fixed PromQL queries.

Enables grading a website's security headers with per-gap fixes and a README badge, and analyzing logs, code, configs or suspicious messages to determine what happened, how severe it is, and what to fix first. Requests are masked for sensitive data and automatically retried across multiple AI providers when one refuses, subject to a defensive-use-only policy.

A lean MCP server that provides LLM agents with transparent access to multiple Prometheus instances for metrics analysis and SRE operations.

Enables LLM clients to manage Uptime Kuma HTTP(S) monitors, groups, tags, and notifications, and to retrieve heartbeats, stats, and incident history through MCP tools.

Provides a unified MCP server to interact with multiple middleware (Loki, MySQL, Redis, RocketMQ, Elasticsearch, MongoDB) through a desktop application for connection management and configuration export.

Read and manage your Argulog error tracking data - issues, projects, alerts, releases, billing, and admin operations - from any LLM agent. Authenticates with a Personal Access Token from the dashboard: PAT scopes gate writes server-side.

Enables AI agents to access observability and evaluation data, including run history, span traces, LLM-as-judge evaluation results, and regression reports.