docs-mcp
by ac.tandem
Tandem is the authority layer for AI-first work: runtime authority for agents, tools, memory, approvals, and audit trails.
MCP
docs-mcp
Added 1 June 2026
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
How to use
Install
npm i -g @frumu/tandem Tested with
Continue, ChatGPT
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.
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
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