secureFlows MCP Server

secureFlows MCP Server

Cloud-deployable MCP server exposing secureFlows' OpenAPI operations (tagged ai-safe/ai-optional) as MCP tools, plus static helper tools like login URL building and integration linting for coding agents.

Category
访问服务器

README

secureFlows MCP Server

secureFlows CI

Cloud-deployable MCP server that wraps the secureFlows OpenAPI surface tagged ai-safe and ai-optional.

This repo is a public mirror, published periodically from the private secureFlows monorepo where development actually happens. Issues and PRs are welcome; large changes may take a release cycle to land upstream first.

What is an MCP server?

An MCP server is a small HTTP service that exposes a set of “tools” an AI client can call in a standard way.

In this repo:

  • The secureFlows MCP server exposes tools that are auto-generated from your OpenAPI YAML specs.
  • When a client calls a tool, the MCP server forwards the call to your real secureFlows backend (connection.host) and returns the response in a normalized tool result.

This lets an AI client:

  • discover available secureFlows operations via listTools
  • call them via callTool
  • without hardcoding the API surface or manual auth/header wiring

What it does

Two kinds of tools, registered together in src/server.ts:

Generated tools (src/tools/build-tools.ts) — one per OpenAPI operation:

  • Loads:
    • docs/openapi/session/secure-flows-session-api.yaml
    • docs/openapi/user/secure-flows-user-api.yaml
    • docs/openapi/docs/secure-flows-docs-api.yaml
  • Exposes only operations tagged ai-safe or ai-optional as MCP tools
  • Forwards requests to a caller-provided secureFlows host — a thin, generic HTTP wrapper with no secureFlows-specific judgment. Every one of these requires a live auth.* token, so they're only useful once a session already exists (see Runtime model below).
  • Maps secureFlows auth headers from MCP tool inputs:
    • auth.firebaseToken
    • auth.sessionToken
    • auth.userToken

Static tools (src/tools/static-tools.ts) — hand-written, not generated from the spec:

  • secureflows_build_login_url / secureflows_build_logout_url — build the hosted-login and redirect-logout URLs correctly by construction (always /app/sessions/login, never the legacy /app/login; refuses a post-logout redirect_uri that points at /callback or leaks session_token). No secureFlows token required.

  • secureflows_lint_integration — checks generated app source against the integration rules and reports structured findings instead of leaving them as prose the agent has to self-police. No secureFlows token required. Two kinds of finding:

    • scope: "file" — a forbidden construct is present, at an exact file:line: env-var config constants, token in localStorage, legacy /app/login, fetch/XHR logout, client-side JWT decode, revoke-on-sign-out, empty catch {}, restore setSession(null) on non-auth errors, Continue CTA gated on session === null, …
    • scope: "project" — required handling is absent across every file passed in: detecting 401/410 but never clearing the token, never handling 403, or handling 403 without the BILLING_GRACE_LOCK carve-out.

    The absence checks exist because the pattern rules structurally could not catch the defect class that dominates real generated apps. Measured: on a real trial's app that the eval harness's LLM judge scored 4/10 — citing "stale token never cleared on signed-out", "403 variants unhandled", "no error handling" — the pattern rules alone produced zero findings, because every one of those bugs is an absence, and a regex can only see what is present. With the absence checks it produces 3, including the error-severity token-clearing one. Both check kinds are validated against the canonical templates/web-app-secureflows starter, which must stay at zero findings.

    Still heuristic text analysis, not a parser or type checker: it misses what it has no rule for, a project check can be satisfied by the right keyword in the wrong place, and it cannot cover the checks that need a running app (auth-guard mount races, the fresh-reload check). A fast first pass — not a replacement for the Agent implementation checklist in SKILL.md.

These static tools exist because the generated tools can't help with the part of an integration that happens before a session exists — scaffolding the redirect/callback/token-lifecycle code — which is exactly where most secureFlows integration mistakes happen.

Uses a stateless HTTP MCP transport, so the server does not persist tenant config or secrets.

Runtime model

Each tool call receives:

  • connection.host: secureFlows base URL
  • connection.workspaceName: optional default workspace
  • connection.appId: optional default application id
  • auth.*: whichever token the selected endpoint needs

workspaceName and appId are treated as stable app config. The server injects them into known secureFlows request shapes when omitted by the caller.

For agents (the only supported client path)

Point the MCP client at the hosted URL — same host as the product, path /mcp (not a subdomain):

Environment MCP URL
Production https://www.secure-flows.com/mcp
Staging https://secure-flows-staging.onrender.com/mcp
Health …/mcp/health{"ok":true}
{
  "mcpServers": {
    "secureflows": {
      "url": "https://www.secure-flows.com/mcp"
    }
  }
}

Do not tell agents to run npx or use localhost — that splits the story and breaks anyone who never starts a local process. Wired in the web Docker image (Node on 127.0.0.1:8787, nginx location = /mcp; see docs/ROUTING.md). The Node process installs uncaughtException / unhandledRejection guards so a single bad request does not exit the process; docker/entrypoint.sh also restarts MCP if the process still exits.

Local development (maintainers of this package)

cd mcp-server
npm install
npm run build
npm test
npm run dev

The server starts on http://0.0.0.0:8787 by default (POST /mcp, GET /health). This is for changing the MCP server itself — not the path product agents should configure.

Environment variables

  • PORT: HTTP port, default 8787 (in the web container, entrypoint sets PORT=8787 only for the MCP child so nginx keeps Render’s public $PORT)
  • HOST: bind host, default 0.0.0.0 (web container uses 127.0.0.1)
  • ALLOWED_HOSTS: optional comma-separated host allowlist for MCP host header validation
  • MCP_ALLOWED_HOSTS: entrypoint override for ALLOWED_HOSTS when starting the in-image process

Endpoints

  • POST /mcp: MCP Streamable HTTP endpoint
  • GET /health: health check (publicly exposed as GET /mcp/health via nginx)

Embedding secureFlows in an application

Product apps integrate directly with secureFlows HTTP APIs and hosted login. Start from:

  • docs/integration/quickstart.md — provisioning (workspace + application) and runtime hosted login
  • docs/integration/CONCEPT.md — baseline order: login → create workspace before advanced features
  • docs/openapi/integration-auth.yaml/app/sessions/login (session apps) vs /app/login (legacy/console)

Product apps still integrate directly with the HTTP APIs above, not through this server. The generated tools here are for agents/automation that already have a token (testing, scripted verification). The static tools (secureflows_build_login_url, secureflows_build_logout_url, secureflows_lint_integration) need no token and are meant to be called by a coding agent while it's still scaffolding the integration — see What it does above.

Testing this MCP server

  1. npm test in mcp-server/ — unit tests plus HTTP smoke (test/http-smoke.test.ts): starts the Express app on an ephemeral port, checks GET /health, GET /mcp → 405, and a real Streamable-HTTP client listTools + callTool(secureflows_build_login_url).
  2. After deploy: Playwright tests/smoke/mcp-health.spec.ts hits public GET /mcp/health and GET /mcp on the target host (production smoke job).
  3. Local maintainer loop: npm run dev, then curl -sS http://127.0.0.1:8787/health.
  4. Optional: MCP client against POST /mcp with connection.host + auth.* for generated tools.

Deployment

Shipped inside the web Docker image and proxied at /mcp on www.secure-flows.com / staging (see For agents above). No separate subdomain.

The npm package secureflows-mcp-server is how CI publishes a versioned artifact (and how a standalone container can be built from mcp-server/Dockerfile); it is not the agent-facing setup path. Publish on v*.*.* tags via .github/workflows/publish-secureflows-mcp-server.yml.

docker build -f mcp-server/Dockerfile -t secureflows-mcp-server .
docker run --rm -p 8787:8787 secureflows-mcp-server

Notes

  • Hosted login / redirect endpoints are exposed only if they are tagged ai-safe or ai-optional in the OpenAPI specs.
  • Documentation search (get_docs_search) is ai-safe, requires no auth.* — only connection.host and query q.
  • Human-only admin console APIs are intentionally excluded.
  • The response payload from each tool includes:
    • status
    • ok
    • url
    • headers
    • data

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选