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