"Observability" matching MCP servers:

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

Gives AI assistants read-only access to a Kubernetes cluster's observability stack — Loki, Prometheus, Tempo, the Kubernetes API — plus AWS telemetry from CloudTrail, Config, ECR, and RDS. 23 tools for querying logs, metrics, traces, Kubernetes events, deployments, and audit history in plain language.

Enables observability and governance for local LLMs via Ollama, including auditing model usage, scanning prompts for secrets/PII, and enforcing allow/deny policies.

Agent observability MCP server for querying OpenTelemetry traces, failures, tool health, security evidence, and regressions.

Safety-first local MCP tool gate with control plane, runtime security, and observability for managing MCP backends.

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.

Exposes Colber's five integrated services (Reputation, Memory, Observability, Negotiation, Insurance) as 27 MCP tools for AI agents, enabling trust, coordination, and continuity.

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.

Enables zero-trust interception, policy enforcement, fuzzing, auditing, external stdio proxying, and visual observability for MCP tool interactions.

An enterprise MCP server scaffold that provides secure, governed access to internal tools through RBAC, audit logging, rate limiting, and prompt-injection boundaries, with a FastAPI control plane and OpenTelemetry observability.

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.

Enables comprehensive management of Tailscale networks including device management, DNS configuration, file sharing via Taildrop, security monitoring, and network automation with built-in Grafana/Prometheus observability stack.

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.

Enables autonomous AI agents to securely access and execute external tools, such as GitHub REST API operations, with per-user authentication, authorization, audit logging, and observability.

Enables MCP clients like Claude Code or Codex to administer a Bifrost LLM gateway's governance API, including virtual keys, teams, customers, budgets, provider configs, routing rules, model/pricing config, request-log observability, and core gateway config.

Provides Model Context Protocol access to the Dynatrace platform API, letting AI agents run live queries against observability, security, and platform data and manage a Dynatrace environment. It is hosted remotely by Dynatrace, so MCP hosts can add it by name from the official registry without installing anything.

Enables deterministic task routing, contract enforcement, and effect authorization across domain owners while exposing a minimal MCP interface and read-only observability.

A remote, Dynatrace-hosted MCP server that gives AI agents live API access to your Dynatrace environment. It enables querying and analyzing observability, security, and platform data — such as DQL queries, logs, traces, problems, and dashboards — so agents can act on production context instead of static knowledge.