docs-mcp

Overview

docs-mcp provides runtime authority for AI agents, tools, memory, approvals, and audit trails. It works as a centralized layer that governs how AI-first applications interact with external resources and execute actions.

Best for

Best for
Developers building secure, auditable AI agent systems that need a runtime authority layer

Use cases

• Enforcing permissions and approval workflows for AI agent actions
• Auditing and logging every tool call and memory access in real time
• Managing secure access to external APIs and data sources for agents

Notes

docs-mcp provides runtime authority for AI agents, tools, memory, approvals, and audit trails. It works as a centralized layer that governs how AI-first applications interact with external resources and execute actions.

105 stars on GitHub. Last updated 2026-05-30.

Use cases

• Enforcing permissions and approval workflows for AI agent actions
• Auditing and logging every tool call and memory access in real time
• Managing secure access to external APIs and data sources for agents

Pros

• Written in Rust for performance and memory safety
• Offers a unified runtime authority model for agents, tools, and approvals
• Open source with a clear focus on AI governance

Cons

• Small community and limited adoption (105 stars on GitHub)
• Documentation and examples may still be sparse for newcomers
• Tied to a specific authority layer concept, which may not fit every workflow

Indexed from mcp-official-registry and enriched against its public facts.