"SearXNG, a privacy-respecting metasearch engine" matching MCP servers:

Prediction stability engine for AI agents. Evaluate model stability, detect ghosts, probe any LLM for instability, monitor fleet drift. 20 tools + 1 resource. Works with Claude, Cursor, VS Code.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

Read-only MCP server for TrueNAS Scale debugging, providing k3s tools for kubectl operations and whitelisted midclt calls for system info, apps, and pools.

Behavioral trust scoring for MCP servers and AI agents. Live registry tracking 4,500+ servers with trust scores (0-100) based on interaction history, success rates, and latency

Enables agents to access web analytics statistics via OAuth, providing a privacy-first, cookie-free analytics service.

Patent-pending diagnostic + decisions-ledger engine for any host software system — operator briefs and a severity-gated MCP console.

Windows tray process that exposes local ambient context (presence, foreground app, battery, etc.) as MCP tools with privacy classification and opt-in controls.

A read-only MCP server that turns Codex task logs into privacy-aware event ledgers and interactive timelines, providing tools to list sessions and retrieve trajectory data with summary or full detail levels.

An MCP server that enables AI assistants to interact with Tenzir, a data pipeline engine for security operations, including executing pipeline queries, managing OCSF schemas, and generating parsers.

Privacy friendly, cookieless web analytics built MCP-first. "Add analytics to my Next.js app" → an AI agent runs the setup_analytics_for_site tool, picks the right install snippet, edits your layout file, and verifies the script is loading. OAuth onboarding, no API keys to paste.

A self-contained, security-first, EU-sovereign unified AI-operations MCP server that fuses a live bitemporal fleet model, a drift engine, and a tiered audited actuator into one control plane.

Expert system hardware probe and performance diagnostic engine for AI, Gaming, and High-Performance workflows. Provides deep system insights such as real-time monitoring, thermal diagnostics, and LLM optimization.

A Model Context Protocol server that provides tools to interact with Matomo Analytics API, enabling management of sites, users, goals, segments, and access to analytics reports through a MCP interface.

This project provides a Model Context Protocol (MCP) server that allows language models and other MCP clients to interact with a SuzieQ network observability instance via its REST API.

Privacy-first, tamper-evident receipts for MCP tool calls. It hashes and signs arguments/results so you can verify what ran, when, without storing raw payloads.

Enables AI assistants to interact with and manage Google Cloud Platform resources including Compute Engine, Cloud Run, Storage, BigQuery, and other GCP services through a standardized MCP interface.

A Model Context Protocol (MCP) server for the Pingera monitoring service, providing seamless integration between AI models and monitoring data.

MCP server for Uptimepage uptime monitoring. An LLM client can read your monitors and incidents, run a check on demand, and post incident updates. Writes need an OAuth login and a scoped token, and each one is logged.

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.