"Observability" matching MCP connectors:
GET /v1/connectors ā MCP directory API referenceMatching Connector Tools:
Read-only Increase banking observability plus one safe non-money-moving write, for AI agents.
Mezmo MCP is a remote Model Context Protocol (MCP) server that lets AI assistants and IDE chat agents interact with the Mezmo observability platform via the Model Context Protocol. Use it for streamlined observability, log analysis, and root-cause analysis in your favorite tools. Add Mezmo MCP and you can: šµļø Run advanced Root-cause analysis over recent logs š¦ List and describe Pipelines š¤ Export and filter Logs with powerful query syntax
Run a prompt through a LangChain (system + human) chain over Gemini on Vertex AI; optional LangSmith
MCP observability: analyse MCP server usage, investigate sessions, and instrument MCP servers.
AI observability, explainability and governance: drift, fairness, ISO 42001, risk register.
Chipp: a deployed project's database (read), logs, errors, analytics, CI status, custom actions.
Mobile observability for AI agents. Investigate crashes, hangs, ANRs, bugs, and app performance, and triage app store reviews, directly from your IDE or terminal.
Structural observability for AI conversations. Detects loops, stuck states, breakthroughs, and convergence across 17 channels without analyzing content.
Observability for AI apps: investigate traces, logs, LLM usage, replays and crashes; manage alerts.
Production observability for AI agents: search runs, read evaluations, acknowledge incidents.
LLM Observability & Orchestration Agent (Langchain)
LLM Observability & Orchestration Agent (LangChain Community)
LLM Orchestration Observability Agent
LLM Observability & Orchestration Agent
Business Observability: process inventory, AP risk, sales temperature, savings. diezX customers only
Trace, evaluate, and optimize your LLM, RAG, and agent apps with Noveum observability.
Manage Kubernetes clusters, deployments, databases, secrets and observability on Mengi Cloud.
AI agent observability for production traces, natural-language insights, and improvement loops.
Data observability tools for engineering teams: alerts, freshness, schema drift, lineage, quality.
Monitor, troubleshoot, and optimize your technology stack with Intelligent Observability.
Query Honeycomb observability data: traces, events, metrics, SLOs, triggers, and boards.
Query application logs, traces, and metrics from your AI coding assistant via Foam's MCP server.
Data + AI observability ā monitor and troubleshoot production-grade agents and the context they use.
MCP observability. Query live traffic, errors, duration, and alerts from your AI agent.
Access New Relic observability data through MCP - query metrics, logs, traces, entities, and more
