"Kyverno policy examples and documentation" matching MCP servers:

Connects policy documents to Claude.ai via vector search, allowing natural language querying and retrieval of indexed documents.

Enables policy-governed MCP interactions with deterministic authorization, tenant isolation, minimized PII exposure, and human approval gates for sensitive mutations, while producing structured audit events.

A collection of Model Context Protocol implementations ranging from a minimal hello-world to an autonomous AI agent for software development.

Automatically analyzes Python codebases and generates professional documentation including README, API docs, and setup guides.

MCP server for Claude Code that generates a documentation skeleton from Git repository changes, outputting a Markdown file with placeholders for AI analysis.

A local-first MCP policy gateway that provides a four-step workflow (plan, approve, execute, read) for a single Apify fixture Actor, enforcing a fixed policy and sanitizing results. It does not call Apify and is designed as a policy control plane.

Generates professional documentation for multi-language projects with deep code analysis, supporting frameworks like Docusaurus, MkDocs, and Sphinx, including API docs, PDF export, and OpenAPI specifications.

A deterministic, network-free MCP server for validating repository release hygiene and version alignment in local projects. It enables automated repository health checks and generates standardized release checklists based on project state.

Generates professional documentation for multi-language codebases with deep AST-based code analysis, supporting Docusaurus, MkDocs, and Sphinx frameworks. Includes API documentation generation, PDF export, OpenAPI spec generation, and sales-ready documentation for code marketplaces.

Converts natural language security requirements into validated Cerbos YAML policies with automated testing and red-team analysis, enabling AI governance with zero-trust guardrails for tool calls, data access, and compliance frameworks.

A governance and policy enforcement gateway for MCP servers that provides hard guardrails, dynamic parameter bounds, human-in-the-loop approvals, and audit telemetry.

Enables onboarding a codebase for AI-agent-assisted development by scanning projects, collecting human-only answers, and maintaining traceable living documentation under .ai/ for both humans and AI coding agents.

Local-first document management and semantic search for AI coding agents, with a built-in web dashboard and REST API.

Analyzes codebases to automatically generate README, API docs, architecture diagrams, and CHANGELOG.

Proxies MCP traffic between a client and a downstream server to enforce runtime policies on tool declarations, call arguments, and results, including allowlisting, sandboxing, secret and egress controls, and injection detection. It also includes a deterministic benchmark for measuring which security controls stop which attacks.

Provides token-efficient access to team policies and standards by allowing subagents to fetch specific sections using § notation, reducing context waste.

Enables natural-language question answering over Australian government policy documents with grounded, source-cited responses and a tool to list indexed documents.

Enables AI assistants to fetch, index, and perform semantic RAG-based searches on API documentation from various sources. It provides tools for hybrid search and collection management, allowing users to access up-to-date documentation from projects like Gemini and FastMCP.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Deterministically evaluates whether a proposed agent spend action matches a supplied policy, returning ELIGIBLE, DENY, or STEP_UP with stable reason codes. Provides local policy evidence only, not payment authorization.