"Open source MCP (Model Context Protocol) implementations" matching MCP servers:

Tracks protocol adherence and maintains audit trails for Claude sessions, providing tools for logging steps, activities, and violations, and computing compliance scores.

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

MCP server providing lossless file reads with deduplication, per-repo token metering, and HMAC-signed context receipts. Enables auditable, vendor-neutral measurement of what an AI agent saw.

MCP server providing deep PostgreSQL context to AI assistants, including schema DDL, index health, foreign key associations, query execution plans, and performance statistics via read-only tools.

Windows tray process that exposes local ambient context (presence, foreground app, battery, etc.) as MCP tools with privacy classification and opt-in controls.

Enables AI agents to access real-time Windows PC context including active window, system performance, screen time, productivity analytics, and historical usage through MCP tools.

Enables assistants to read-only explore and analyze Grafana dashboards and data sources, execute queries against Prometheus and Azure Log Analytics, and run guided investigation workflows.

A verifying MCP gateway that pins approved tool manifests and skill files to content addresses, blocking tampered descriptions before they reach model context.

Provides a read-only interface to Kubernetes clusters, enabling LLMs to list pods, get pod status and logs, fetch deployment manifests, and perform pod health analysis with resource trend tracking.

Privacy-first, tamper-evident receipts for MCP tool calls. It hashes and signs arguments/results so you can verify what ran, when, without storing raw payloads.

Multi-machine system monitor with a built-in MCP server that enables AI agents to query health metrics, manage processes, schedule cron jobs, and run diagnostics across local and remote machines.

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.

Reads local Codex rollout logs to expose context-window usage snapshots, remaining-token estimates, and log-event freshness through a read-only MCP tool.

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

A feature-rich gateway and proxy that federates MCP, REST, and gRPC services into a unified endpoint for AI clients. It enables virtualization of legacy APIs as MCP-compliant tools while providing built-in security, rate-limiting, and OpenTelemetry observability.

The policy enforcement plane for MCP: deterministic admission and egress policy, attributable audit, and SIEM export for your MCP server fleet. Self-hosted and MIT licensed.

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.

Radar is an open-source Kubernetes observability and diagnostics MCP server. It exposes cluster health, workload diagnosis, logs, events, topology, audit findings, and remediation actions to AI agents through the Model Context Protocol.

A Model Context Protocol (MCP) server that lets an AI assistant read and operate the Lumics network monitoring platform via its REST API.

Enables building, validating, describing, and conformance-testing TTEOP telemetry envelopes through the Model Context Protocol.