Docs Mcp
Remote MCP server for Tandem docs, install guides, SDKs, workflows, and agent setup help.
- Transport
- Not stated
- Package
- —
- Registry id
- ac.tandem/docs-mcp
No install snippet on purpose. A working MCP config is a command, its arguments and an environment block — the last two are where API keys live, so this catalogue never stores them and cannot publish them. Follow the link above for the authors' own instructions.
Tandem enforces policy between AI agents and the tools, data, memory, and actions they use.
Agents can reason, draft, and propose work. Tandem decides what they are authorized to see, which tools they can call, which actions must pause for approval, what memory/context they can access, and what evidence gets recorded.
This makes Tandem useful when agents touch real company systems: files, repositories, email, MCP tools, customer data, internal docs, production workflows, and long-running automations.
For platform and security teams, Tandem acts as a runtime control plane for agentic systems: scoped tool access, approval gates, permissioned memory, tenant/resource boundaries, and audit evidence.
The model proposes. Tandem enforces.
In governance terms, Tandem manages delegated authority for AI agents at runtime.
What Tandem Does
- Runs AI workflows with durable state instead of transcript-only execution.
- Scopes which built-in tools and MCP connectors are visible at each workflow step.
- Blocks tool calls that fall outside runtime policy before execution.
- Pauses consequential actions for human approval.
- Controls which company memory and context a run can retrieve.
- Records artifacts, tool events, approval decisions, and audit evidence outside the model context window.
Simple Example
An agent may be allowed to draft a customer email, but not send it.
Tandem can expose the draft tool, hide or block the send tool, pause at an approval gate, resume only after a human approves, and record the decision in the audit trail.
What Tandem Is Not
| Not this | Instead |
|---|---|
| Chatbot wrapper | Runtime layer underneath agents and workflows |
| Agent framework only | Policy layer that controls what agent workflows can see and do |
| Approval UI only | Runtime enforcement with approvals as one controlled gate |
| LLM gateway only | Governs workflow state, tools, memory, approvals, artifacts, and audit |
| Flat RAG system | Runtime-scoped memory and source-bound retrieval |
| Prompt-only safety layer | Enforcement happens outside the model |
Tandem calls this runtime authority: authorization, execution control, approval, scoped memory, and audit enforced outside the model. Entrypoints such as the desktop app, TUI, web control panel, channels, and SDKs are clients of the same engine runtime.
- Runtime-owned controls: Runs, sessions, memory, context, provider secrets, MCP tools, approvals, artifacts, and audit records live outside the model.
- Governed tool execution: Built-in tools and MCP connectors can be scoped per workflow step, with approval gates for consequential actions.
- Tenant-aware runtime: Hosted and enterprise modes carry tenant/principal context through sessions, runs, context runs, memory, provider credentials, MCP secrets, and events.
- Deployable where the data lives: Tandem can run locally, headlessly, hosted, or inside customer infrastructure.
- Provider agnostic: Use OpenRouter, Anthropic, OpenAI, OpenCode Zen, or local Ollama endpoints.
Agent intent -> Runtime policy -> Scoped tool/data access -> Approval gates -> Artifacts -> Audit trail
-> AI runtime infrastructure | Enterprise readiness | Runtime trust boundaries | EU AI Act readiness | Compliance starter pack | Connect an agent via MCP
Why Tandem Exists
From the project's README.