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Observability

Tools and platforms for comprehensive system observability including logs, metrics, and traces. Enables monitoring, troubleshooting, and performance analysis of applications and infrastructure.

MCP ServersBrowse all →

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    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.
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    Apache 2.0
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    Read-only PostgreSQL performance auditing via MCP. Collects metrics from PostgreSQL system views, applies deterministic checks for index, query, vacuum, configuration, and connection issues, and returns structured findings for MCP clients.
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    MIT
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    Enables LLMs to query telemetry data via the Spyglass AI agent, providing intelligent insights about application performance, errors, and bottlenecks.
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    Enables AI assistants to query global BGP routing intelligence, including network investigation, origin history, reachability, and health checks, with 15 tools plus resources and prompts for incident triage and reporting.
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    MIT
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    Model Context Protocol server for Verlon AI that exposes gates, logs, recommendations, and experiments as MCP tools, enabling coding agents to inspect and manage AI infrastructure natively.
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    MIT
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    This MCP server enables AI assistants to read PBX state—extensions, DID numbers, SIP trunk providers, tenants, and live statistics—from ICTPBX via the ICTCore REST API, without placing calls or changing configuration.
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    MIT
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    Uptime monitoring for websites, APIs, SSL certificates, domain expiry, ping and TCP/UDP ports. 15 tools to list, create, pause and delete monitors, pull incident timelines with error codes, and read hourly or daily uptime and response-time statistics.
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    Enables coding agents to read performance findings from sixty, get detailed evidence for each finding, and close or resolve them with descriptions, while also checking service telemetry and providing installation instructions.
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    MIT
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    MCP server for measuring web performance with Google PageSpeed Insights, providing median scores with uncertainty and real-user data. It offers tools for scores, diagnostics, and comparisons to identify regressions.
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    Apache 2.0
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    8 standalone MCP servers for SMBs — Guardrails (prompt injection, PII, secrets detection), FinOps (AI cost tracking and budgets), Observability (agent tracing), Smart Router (17+ models, 9 providers), Trust Score (governance scoring 0-100), Memory (persistent agent memory), ThinkSecure (runtime security + SOC2/GDPR audit), and A2A Lite (agent-to-agent protocol). 37 tools total, TNC credits
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    RSigma is a Rust detection-engineering toolkit for the Sigma standard. Its MCP server (rsigma mcp serve) exposes the toolchain to AI agents over stdio or Streamable HTTP, with tools to author, lint, validate, and convert Sigma rules, evaluate and explain detections against log events, and inspect correlation state.
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    MIT
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    A Model Context Protocol server that enables LLMs to retrieve and analyze OpenTelemetry traces and metrics from Logfire, supporting exception tracking and custom SQL queries against telemetry data.
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    MIT
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    Enables LLMs to analyze and manage DBOS workflows, including introspection, workflow management, and authentication, to help debug applications in development or production.
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    MIT
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    Enables MCP clients to query an organization's Traceorb telemetry data through the same GETs the panel uses, using a read key for authentication.
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    Enables interaction with Foreman instances to manage systems through the Model Context Protocol. It provides access to Foreman resources and tools, such as security update reports, directly within AI-powered environments like VSCode and Claude Desktop.
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    GPL 3.0
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    Connects AI assistants to Crontinel, enabling natural language queries about cron jobs, queues, Horizon status, and alerts without opening a browser.
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    Enables data observability operations with the Sifflet platform. Supports exploring assets, monitors, incidents, generating monitor-as-code YAML from descriptions, and performing impact analysis.
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    Enables language models to access LangSmith observability platform features including fetching conversation history, managing prompts, retrieving traces and runs, working with datasets and examples, and analyzing experiments.
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    Enables AI agents to query Aptabase analytics through MCP, including metrics, top events, countries, sessions, and more. Uses the dashboard's cookie-authenticated endpoints, so it requires an Aptabase auth session cookie.
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    Enables AI agents to interact with Freeplay, an ops platform for AI engineering teams, to analyze production logs, identify quality issues, iterate on prompts and agents using real data, and run experiments before deploying.
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    Apache 2.0

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