"Writing about MCP" matching MCP servers:

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Gives Claude live access to your Observe tenant, enabling natural language queries about errors, logs, and metrics without writing OPAL pipelines.

Provides Claude with read-only access to PC diagnostics including hardware specs, live performance metrics, disk usage, network checks, and more, enabling natural language queries about PC health and performance.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

MCP server that provides read-only analysis of Claude Code session transcripts, enabling queries about project activity, session details, and tool usage.

Sigma detection rule writing, validation, and pySigma-based multi-backend conversion (Splunk, Elastic, Wazuh, Kibana) via 3 MCP tools and 3 Claude Code skills, backed by a 61-rule production corpus across 11 MITRE ATT\&CK tactic categories.

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

An MCP server that provides offline searchable Microsoft 365 roadmap data and live Azure status, enabling AI agents to answer questions about upcoming features, roadmap changes, and Azure incidents.

Enables AI agents to push and read metrics from EzStat, track counters/gauges, and ask natural-language questions about telemetry.

Enables read-only inspection of local coding-agent sessions across Claude Code, OpenCode, Cursor, and Codex, including running status, topics, todos, and transcripts, without writing or sending messages.

Provides AI agents with real-time ground truth about what's actually running on the machine, including live agent sessions, listening TCP ports, loaded daemons, and system stats. It reads directly from the OS via fixed commands, ensuring agents never rely on stale docs or transcripts.

Connects AI assistants to Crontinel, enabling natural language queries about cron jobs, queues, Horizon status, and alerts without opening a browser.

Read-only MCP server that exposes Kubernetes platform state (tenants, pods, SLOs, ArgoCD applications, chaos schedules, and catalog services) to AI agents, enabling natural language queries about cluster health and configuration.

Enables LLMs to query telemetry data via the Spyglass AI agent, providing intelligent insights about application performance, errors, and bottlenecks.

Ask your AI agent about your MCP servers: which tools fail, how a release compares, what clients look for and do not find. Reads from mcpspan with a read token.

MCP server that wraps Cursor Enterprise API to enable AI agents to answer questions about team spending, usage, and model adoption.

The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

Enables an AI assistant to answer plain-language questions about a TP-Link Omada network — why Wi-Fi drops, which switch port a device is on, which VLAN it uses, and whether radar or interference is at play — by reading switches, access points, SSIDs, clients, topology and logs through 59 tools. It stays read-only unless two separate gates are opened, and any change is dry-run first, then read back and diffed.

A Model Context Protocol server that provides AI agents with controlled read access to Datalust Seq instances for log analysis and monitoring. It enables agents to search events, execute data queries, and retrieve information about signals, dashboards, and alerts.

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 transparent security for any MCP server by intercepting tool calls, blocking prompt injection attempts, masking PII in responses, and writing immutable audit logs.

Enables LLM clients to ask natural-language questions about FABRIC Testbed infrastructure metrics, with responses grounded in fixed Grafana/PromQL queries for node, switch, and link metrics.

An MCP server that connects Claude (or any MCP compatible client) to your existing log infrastructure. Query, summarize, and trace logs in plain English across GCP Cloud Logging, AWS CloudWatch, Azure Log Analytics, Grafana Loki, and Elasticsearch without writing filter expressions or leaving your editor.

Self-hosted web analytics platform that exposes analytics data via the Model Context Protocol, enabling natural language queries about traffic, page views, web vitals, and custom events.

Enables an AI assistant to diagnose on-premise Windows pm2-managed Node/Nitro services by answering read-only questions about deployed builds, process liveness, logs, current configuration, and whether the running process started with the configuration currently on disk.

Provides Claude with access to Shodan for IP address lookups, service discovery, and vulnerability scanning, allowing users to query information about hosts, count results, and check API usage.

Enables users to ask about render performance in React and React Native apps via natural language, exposing tools for listing render hotspots and explaining component re-renders.

Enables local-first tracking of AI token usage and costs from Claude Code and OpenCode, answering queries about tokens, models, sessions, and cost through MCP tools and a CLI, with no network calls.

Provides read-only access to query and retrieve information about devices, fleets, events, and configurations managed by Flight Control through a safe integration layer supporting filtering and selector-based queries.

Enables Alexa+ to answer plain-English questions about a home network using live diagnostics such as ping latency, bandwidth, connected devices, and interface health, with optional local or AWS Bedrock LLM reasoning and safe service restart tools.

Enables tracing of synthetic events through a toy Redis Streams + Postgres ingestion pipeline, answering questions about which events were dropped, duplicated, delayed, or are in flight, plus overall pipeline health. It also supports first-class fault injection and reconciles observed pipeline state against independently recorded producer ground truth to expose real loss or silent failures.

Enables LLM agents to answer questions about Darshan I/O logs by first opening a log and then retrieving exact, tool-computed numbers: canonical per-concern cards (volume, bandwidth, request sizes, metadata, rank balance, Lustre, DXT, verdict), an outline/search/get interface over a two-layer schema, and exact arithmetic, so answers are retrieved rather than estimated.

Captures DNS queries leaving your Mac and serves them to Claude via MCP for traffic pattern analysis, answering questions about top domains, categories, and temporal patterns.

Enables natural language queries about device status, network traffic, sensor readings, alerts, and historical trends from Observium CE network monitoring.

Enables integration with Datadog APIs to monitor and retrieve information about monitors, metrics, dashboards, logs, events, and incidents through the Model Context Protocol.

MCP server for writing structured traces, spans, and decisions, enabling observability and EU AI Act traceability compliance for AI agent tasks.

Enables AI assistants to query and fetch error and performance monitoring data from AppSignal through the Model Context Protocol. Supports searching and retrieving detailed information about application errors and performance samples with flexible filtering options.

A local MCP server that lets Claude answer plain-language questions about your Anthropic usage and spend across the developer API platform and Claude Enterprise products.

Enables comprehensive system monitoring and diagnostics through 18 tools that provide detailed information about CPU, memory, disk usage, network interfaces, running processes, battery status, hardware details, and temperature monitoring. Allows users to query system information and performance metrics through natural language interactions.

Mnemosyne is an MCP server that gives AI assistants deep visibility into Kubernetes clusters, enabling natural language queries about cluster state, pods, services, and more.