"A GitHub repository for agent guardrails" matching MCP servers:

Enables real-time assessment of GitHub repository health by exposing tools and resources for repository metadata, open issues, recent commits, and GitHub Actions workflow runs, plus prompts for health reports and issue triage.

Monitors and analyzes GitHub repository health by detecting stale branches, old pull requests, unresponsive issues, and security alerts. Integrates with MCP-compatible AI assistants and automation tools.

Registers a set of inert, tempting decoy tools with any MCP-capable client so that any call from a hijacked or prompt-injected agent instantly raises an alert with full attack context and a per-call trace token. Also plants worthless canary tokens in honeypot files that trigger zero-false-positive leak alerts when they surface in agent output, files, or outbound requests.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

An unofficial MCP server that connects AI assistants to Akamai APIs, providing tools for edge diagnostics, debugging, and network analysis.

A minimal MCP server that exposes health-check and server information tools over stdio or HTTP, and can be containerized and deployed to IBM Code Engine.

MCP server exposing AI agent observability tools: list, inspect, and deterministically replay recorded agent execution traces for debugging failures.

Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.

Brings live project context into Slack via MCP, enabling developer teams to check service health, recall team decisions, search code, and query project context directly from chat.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

Enables AI agents to self-report every tool call, token usage, decision, and session grade into a local SQLite store, so users can trace execution, audit reasoning, and see per-session costs in a local dashboard. Also supports proxy mode to intercept and trace MCP tool invocations for agents that cannot self-report.

An MCP server that provides append-only, tamper-evident local receipts for AI agent actions, capturing command executions, outputs, and handoff evidence.

Shows your current Claude Desktop activity as Discord Rich Presence, including model, status, usage, and subscription info.

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

Exposes network-monitoring tools (query metrics, analyze windows, compare, logs, status, runbooks, speed tests) as an MCP server for agentic workflows. Designed with evaluation suites, cost-aware model routing, and semantic tool retrieval.

Provides cost analysis of AI agent usage by previewing scrubbed metadata before upload and analyzing spend, cache-hit rates, and waste recovery through a local or remote server.

MCP server for AI agent observability, providing trace and span logging, search, latency/tokens/cost metrics, and anomaly detection using an in-memory buffer.

Enables AI agents to manage prompts and sessions via the ai-agent-logs API, with tools for health checks, login, listing/creating prompts, and retrieving sessions.

Tracks AI agent token usage and spending in real time, with budget alerts, per-task cost breakdown, and a visual dashboard.

Unified MCP safety server that detects prompt injection (75 patterns), scans LLM outputs for leaked secrets/PII, enforces API cost budgets, and creates signed audit trails. Zero ML dependencies, pure Python.