Docs MCP

Name: Docs MCP
Author: frumu-ai

作者：frumu-ai版本 0.3.2i★ 125i在云端运行

注册名称ac.tandem/docs-mcp

安装

概览

作者提供的说明（英文原文）

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i5 项中通过 4 项依据公开信息检查；尚未扫描代码
使用情况i起步阶段★ 125

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在云端运行
传输方式: Streamable HTTP
分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：5 项中通过 4 项
在作者的服务器上运行：你向它提出的内容会经过他们的服务器。
可以做 13 件事（工具） search_docs, get_doc, get_start_path, recommend_next_docs …
注册中心没有列出需要填写的密钥。

你需要

在云端运行无需安装任何东西，通过互联网连接

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

适用于网页版、桌面版和手机版 Claude：

打开 Settings → Connectors
点击 Add custom connector
粘贴下面的地址，然后点击 Add
如果要求登录，请登录作者的服务

https://tandem.ac/mcp

在终端中运行这条命令

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

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

ChatGPT 只能连接互联网上的服务器：

打开网页版 ChatGPT → Settings → Connectors (Apps)
点击 Create 或 Add（部分套餐需要先在 Advanced 中开启 Developer mode）
粘贴下面的地址，起个名字，然后保存
如果要求登录，请登录作者的服务

https://tandem.ac/mcp

菜单名称可能因套餐和版本不同而略有差异。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

工具名称和描述与服务器在 2026年11月6日报告的完全一致。这些内容由作者撰写，不是我们写的；我们以纯文本形式显示。

search_docs
Search the published Tandem docs index for relevant pages.
get_doc
Fetch and normalize a published Tandem docs page by path or URL.
get_start_path
Recommend the best Tandem docs starting path for a desktop user, server admin, or developer.
recommend_next_docs
Recommend the next Tandem docs to read after a current page, optionally biased toward a goal.
answer_how_to
Answer how to install or use Tandem, build workflows, or use the SDKs by synthesizing relevant published docs.
get_tandem_guide
Return a structured Tandem learning guide for install-engine, python-sdk, typescript-sdk, build-workflows, or headless-deploy.
warmup_docs_cache
Refresh the docs index and preload the most important Tandem docs pages into cache.
get_docs_cache_status
Inspect the current docs cache state for index sources and warmed pages.
refresh_docs_index
Force-refresh docs indexes and report whether data came from the network, cache, or stale fallback.
refresh_doc_page
Force-refresh a docs page and report whether content came from the network, cache, or stale fallback.
invalidate_docs_cache
Invalidate docs cache entries for index data, all pages, or a specific page path.
compare_docs_index_refresh
Force-refresh the docs indexes and return before/after hashes so content changes can be proven.
compare_doc_page_refresh
Force-refresh a docs page and return before/after hashes so page content changes can be proven.

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过域名 tandem.ac 验证（注册中心会核实域名所有权）
✓ 通过
仍在维护
代码最后更新于32天前
! 注意
有许可证
GitHub 无法识别许可证类型；请打开文件自行查看
✓ 通过
端点有响应
在 260 毫秒内响应（HTTP 200）
– 不适用
使用 OAuth 登录
无需登录
✓ 通过
工具描述中没有隐藏指令
已读取 13 个工具描述，未发现针对 AI 的隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7264 个服务器的 100%
发布到注册中心的时间比此分类中其他 7264 个服务器的 10% 更新
基础检查结果等于或优于此分类中其他 7264 个服务器的 40%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Docs MCP 安全吗？

它在 5 项适用的基础检查中通过了 4 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在作者的云端运行。通过互联网连接，无需安装，但你发送的内容会经过作者的服务器。

它还在维护吗？

GitHub 仓库最后更新于32天前，最新版本（0.3.2）于7个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接，所以接受远程 MCP 服务器的应用也可以使用，例如开启开发者模式的 ChatGPT。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新

安装

Codebase Memory

作者：DeusData

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

在你的电脑上运行★ 48,148MIT21天前更新

安装

Exa

作者：exa-labs

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

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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

只显示了开头部分；其余内容请在仓库中阅读