"Observability" matching MCP servers:

MCP-native agent evaluation and observability server. Log traces, evaluate output quality with 12 built-in rules (PII detection, prompt injection, cost thresholds), and track agent costs. Real-time dashboard, OTel-compatible spans. Self-hosted, MIT licensed.

Semantic tool-routing gateway for MCP servers that cuts 70-90% of context tokens, with RBAC, API-key auth, response caching, hot-reload config, and a real-time observability dashboard.

A FastMCP server providing real-time weather, news retrieval, and local note management tools for autonomous CrewAI agents. It enables context-aware multi-agent workflows with observability and high-speed inference integration.

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 AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

Provides comprehensive documentation about AWS AgentCore framework to GenAI tools, enabling users to build production-ready AI agents with enterprise-grade security, observability, and scalability. Offers guidance on identity management, API integration, monitoring, code execution, memory storage, and tool integration for AI agents.

A comprehensive gateway and registry that unifies MCP, REST, and Agent-to-Agent services into a single secure endpoint for AI clients. It provides advanced capabilities including federation, virtual servers, rate-limiting, and OpenTelemetry observability.

Enables local, turn-level governance observability for agent harness transcripts by ingesting sessions, judging each turn against workspace operating rules, and serving a viewer that shows compliance and token flow without sending data off-machine.

Enables comprehensive integration with Azure AI services including OpenAI, Cognitive Services, Computer Vision, and Face API through a mission-critical MCP server. Provides enterprise-grade reliability with high availability, observability, chaos engineering, and secure multi-region deployment capabilities.

Maple is a unified MCP server for agent observability, safety control, and behavior evolution, acting as a monitoring and auditing layer for high-agency agents. It enables developers to capture session timelines, replay risky branches, detect anomalies using ML, and enforce action guardrails.

Enables per-profile tool filtering and enforcement for one or more upstream MCP servers, exposing only allowed tools and rejecting blocked calls, with Streamable HTTP serving and observability.

Provides a centralized MCP gateway with connection pooling, real-time observability dashboard, and macOS menu bar integration, enabling efficient resource usage for AI coding clients.

An MCP orchestration server that coordinates multiple AI coding agents using Copilot CLI for parallel task execution with dependency management, file locking, and observability.

MCPWatch: observability for MCP servers. One line instruments any Python MCP server (FastMCP and the low-level Server) and tracks per-tool latency (p50/p95/p99), error rates, silent failures (empty/null returns and isError), and call volume. REST API, CLI, and alert hooks. MIT, no config required.

A production-ready unified entry point for AI agents that implements the Model Context Protocol (MCP). It provides a secure gateway with rate limiting, authentication, and observability for managing and proxying requests to multiple downstream APIs.

Federates MCP, A2A, and REST/gRPC APIs with centralized governance, discovery, and observability, optimizing agent and tool calling with plugin support.

Enables Claude Code to operate as an autonomous multi-agent company through MCP tools for task orchestration, agent memory, meetings, tool governance, and persistent workflow observability.

Enables secure enterprise AI agents to access internal tools like GitHub, Gmail, Calendar, file systems, databases, and knowledge bases through the Model Context Protocol, with built-in security, audit, and observability.

An observability tool for agentic AI pipelines that intercepts MCP and Python tool calls to provide real-time metrics, session replays, and alerts via a local dashboard. It enables centralized monitoring of multiple MCP servers through multiplexer and proxy modes without requiring changes to existing agent code.

A unified MCP server providing observability, safety control, and behavior evolution for high-agency AI agents through tracing, replaying, and auditing. It features real-time firewall guardrails and ML-driven anomaly detection to monitor, block, or fork agent actions based on risk.

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.

An open source registry and proxy that federates MCP, A2A, and REST/gRPC APIs with centralized governance, discovery, and observability. It optimizes agent and tool calling, and supports plugins.

Self-hosted MCP governance gateway that aggregates 150+ tools behind policy profiles (strict/balanced/dev) with a per-call audit trail. Python 3.12/FastAPI, Docker-deployable, built for regulated environments that need observability over agent tool calls.

An MCP server that provides a secure, sandboxed tool execution fabric for AI agents, featuring JSON-RPC 2.0 protocol support, read-only constraints, and an observability inspector dashboard.

Enables MCP clients to discover and execute operational tools such as database mutations and system diagnostics over stdio, with deterministic structured outputs and Langfuse observability.

Local-first observability for AI agent stacks. Provides MCP tools for agents to check health, shadow configs, and burnout metrics proactively.

Enables autonomous context window management, prompt caching, token optimization, and real-time observability for AI coding agents across multiple IDEs, reducing token costs and improving performance.

Turn a PowerLab homelab/enterprise host into a structured, agent-driven surface for catalog browsing, app management, system observability, and compose-yaml authoring.

A unified gateway for AI agent tools that provides a single MCP stdio endpoint for executing tool calls with unified auth, rate limiting, and observability. Enables agents to interact with multiple external APIs through a standardized interface.

Enterprise-grade MCP server with multi-agent system for business AI transformation across finance, healthcare, retail, and other domains. Provides specialized AI agents for data analysis, API execution, business validation, and report generation with real-time monitoring and observability.