"Most Used Minecraft Plugins (MCP)" matching MCP servers:

Enables diagnosing long AI-agent traces by identifying the most likely root-cause steps, so users can quickly find where an agent run went wrong.

Enables checking and validating disk space usage via a single tool, reporting total, used, and available space with optional free space thresholds.

MCP server that reads Spark profiler files to give accurate Minecraft server tuning advice, parsing binary protobuf directly and diagnosing TPS, MSPT, GC, heap, and call-tree issues.

Enables querying Munin RRD time-series data through natural language, with tools to discover hosts/plugins, fetch raw or aggregated metrics, and render graphs.

MCP server for Minecraft modpack diagnostics, enabling mod conflict detection, log/crash analysis, and dependency lookup via Modrinth.

Evidence-first MCP server for diagnosing problems with hooks, plugins, skills and configuration, returning ranked probable causes and reviewable fixes without modifying the workspace.

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.

Enables an LLM agent to investigate a Kong Gateway through fourteen read-only, schema-validated tools and a deterministic diagnostic engine, so it can trace routes, services, plugins and upstream health and explain faults such as why authentication is failing on a path. Every fact comes from the gateway itself, and the model only selects tools and writes the evidence-backed explanation.

MCP server that exposes plugin crash reports from a crash reporting site to Claude, enabling querying and managing crash data via tools like listing frequent crashes, searching, adding comments, and managing plugins.

Enables agents to report and query performance metrics to build a community-driven quality database for MCP tools. This server helps agents discover and select the most reliable tools based on success rates and user-reported quality scores.

Connects AI assistants to OpenTelemetry trace backends such as Jaeger, Tempo, Traceloop, and Datadog, enabling natural-language searching of traces and spans, error discovery, and service listing. It also provides LLM-specific analytics, including token usage aggregation, model performance comparison, and identification of the most expensive or slowest traces.

Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.

A fully compliant MCP gateway and registry that federates any MCP, A2A, or REST/gRPC API behind one unified endpoint, adding centralized discovery, auth, rate limiting, retries, and OpenTelemetry observability. It virtualizes legacy services and agents into MCP-compliant tools, prompts, and resources, with 40+ plugins extending transports and protocols.

Enables an AI assistant to diagnose a local WordPress install by listing plugins, checking PHP/WP environment, tailing debug logs, running guarded read-only SQL queries, and inspecting Gravity Forms error and add-on logs.