Docs MCP

Name: Docs MCP
Author: frumu-ai

by frumu-aiversion 0.3.2i★ 125iRuns in the cloud

Registry nameac.tandem/docs-mcp

Overview

Description from the maker (original English)

Remote MCP server for Tandem docs, install guides, SDKs, workflows, and agent setup help.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi3 of 4 passedChecked from public information; code not scanned yet
UsageiEarly★ 125

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs in the cloud
Transport: Streamable HTTP
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

You need

Runs in the cloudNothing to install; it connects over the internet

Claude DesktopClaude CodeCursorVS Code

Open Claude Desktop → Settings → Connectors → Add custom connector, and paste this address

https://tandem.ac/mcp

Run this in your terminal

claude mcp add --transport http docs https://tandem.ac/mcp

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "docs": {
      "url": "https://tandem.ac/mcp"
    }
  }
}

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "docs": {
      "type": "http",
      "url": "https://tandem.ac/mcp"
    }
  }
}

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 5, 2026

✓ Pass
Maker matches the repo
The registry name was verified against the domain tandem.ac (the registry checks domain ownership)
✓ Pass
Still maintained
Code last changed 30 days ago
! Caution
Has a licence
GitHub could not identify the licence type; open the file to check
✓ Pass
Endpoint responds
Responded in 931 ms (HTTP 200)
– Not applicable
Sign-in with OAuth
No sign-in needed
… Not checked yet
No hidden instructions in tool descriptions
Needs the server’s tool list first; this check starts in the next step

Compared with others in Search & AI memoryi

More stars than 20% of the other 10 servers in this category
Published to the registry more recently than 10% of the other 10 servers in this category
Basic checks equal to or better than 0% of the other 10 servers in this category

Compared only with servers in this prototype, not the whole registry.

Common questions

Is Docs MCP safe?

It passed 3 of 4 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.

Is it still maintained?

The GitHub repo last changed 30 days ago, and the latest version (0.3.2) reached the registry 7 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 2 days ago

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 19 days ago

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 43 days ago

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

English | 简体中文

Interested in Tandem Hosted? Join the waitlist

Tandem

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](docs/AI_RUNTIME_INFRASTRUCTURE.md) | [Enterprise readiness](docs/ENTERPRISE_READINESS.md) | [Runtime trust boundaries](docs/RUNTIME_TRUST_BOUNDARIES.md) | [EU AI Act readiness](docs/EU_AI_ACT_COMPLIANCE.md) | [Compliance starter pack](docs/compliance/README.md) | [Connect an agent via MCP](https://tandem.ac/docs-mcp)

Why Tandem Exists

Agents are becoming workers. They read company context, call tools, open pull requests, draft customer communication, operate project boards, and prepare decisions that used to stay inside human-only systems.

Prompts are not permissions. A system prompt can ask a model to avoid a tool, skip a folder, or wait for approval, but the model should not be the security boundary. Tandem puts those controls in the runtime, so a workflow can grant the agent only the tools, memory, and actions needed for the current step — and deny anything outside that scope.

Companies also need central AI context without flat access. A permissioned company memory should know what the company knows, but an agent acting for one team, tenant, project, or user should only retrieve the slice it is allowed to use.

What Tandem Governs

Company knowledge and memory: Runtime-owned memory, knowledge spaces, and retrieval paths designed around tenant and workspace boundaries.
Tool and MCP visibility: Step-scoped built-in tools and MCP connector tools, with broader pre-invocation masking planned for enterprise deployments.
Workflow execution: Durable automation and context-run state instead of fragile transcript-only execution.
Human approvals: Runtime gates pause runs, collect approve/rework/cancel decisions, and leave evidence.
Tenant and workspace boundaries: Tenant-aware sessions, runs, context runs, events, provider credentials, MCP secrets, memory, and contract vocabulary for resource scopes and grants.
Connector credentials and secrets: Provider and MCP secret references are runtime-owned; connector source binding gives scoped ingestion a shared contract as that layer matures.
Artifacts and audit trails: Outputs, validations, tool ledger events, approval decisions, and protected audit records survive outside the model context window.

Core Use Cases

Use case	What Tandem adds
Approval-gated email and workflows	Agent proposes work, Tandem pauses before the action, a human approves or requests rework.
Permissioned company knowledgebase	Company memory and knowledge spaces with tenant-aware retrieval and resource-grant vocabulary.
Governed coding agents	Coder runs, worktree context, handoff artifacts, approval points, and auditable implementation state.
Project, sprint, and event brain	Long-running context, tasks, artifacts, and memory that survive across sessions and teams.
Tenant-isolated hosted automations	Hosted runtime records, event streams, provider credentials, MCP secrets, and memory scoped by tenant.
Internal agent and tool governance	A control point for which agents can see which tools, execute which actions, and leave which evidence.

Why Platform And Security Teams Care

Tandem is designed for teams that need to run AI work under real operational controls:

Runtime authority, not prompt authority: The model can request context or a tool call; the runtime decides what is visible and executable.
Tenant-aware records: Sessions, automation runs, context runs, event streams, provider credentials, MCP secrets, and memory paths carry tenant context in hosted/shared modes.
Resource and grant model: Tandem models resources, principals, grants, data classes, and data boundaries so access decisions can be enforced by the runtime.
Permissioned memory: Memory and knowledge paths carry tenant boundaries so company knowledge can become useful without becoming globally flat.
Deployable runtime: The same runtime can run on a laptop, as a headless engine, hosted, or inside customer infrastructure as the enterprise layer matures.
Auditability: Approval decisions, policy denials, provider secret changes, MCP activity, tool ledger events, artifacts, and protected audit records can be inspected outside chat transcripts.

Deployment Model

Tandem is useful locally and grows toward stricter company deployments:

Local desktop: Single-user desktop runtime with local workspace scope, provider setup, and approval-gated tools.
Headless engine: tandem-engine serve for SDKs, control panels, automations, and CI/dev environments.
Hosted/private managed: Hosted deployments with transport-token and signed context assertions for tenant-aware access.
Customer infrastructure: A deployment model for running where company data, connector credentials, and operational evidence need to live.

Current Status

Current capabilities	Enterprise roadmap
Runtime auth modes: `local_single_tenant`, `hosted_single_tenant`, `enterprise_required`	Full RBAC, OIDC, SCIM, SIEM integrations, SOC2 package, and enterprise identity policy bridge
Tenant context and signed context assertions for hosted/enterprise ingress	Private enterprise sidecar with fail-closed policy authorization
Tenant-aware sessions, automation runs, context runs, events, coder routes, and memory APIs	Complete artifact/export isolation across every path
Provider credential and MCP secret tenant boundaries	Full tool-discovery masking before model invocation
Memory tenant partitioning, tenant-scoped knowledge spaces, and resource-scoped retrieval APIs	Production connector ingestion admin platform with live external source ingestion

| Resource access-control contract types and strict context projection vocab

Only the beginning is shown; read the rest in the repo