Maango-mcp

Maango-mcp

Maango is the pre-flight check for AI agents on the web. Before an agent scrapes, summarises, trains on, or searches a site, it calls Maango and gets back whether the action is allowed for that domain, along with the reason and the policy signals that decided it.

Category
Visit Server

README

Maango MCP Server

CI License: MIT Python 3.10+ Docker (GHCR)

The permissions layer for AI agents on the web. Before your agent scrapes, summarises, trains on, or otherwise uses content from a website, ask Maango what's allowed. One call, canonical answer.

Why

Every site that publishes a robots.txt, ai.txt, llms.txt, TDM-Rep header, or AI-specific ToS clause is telling agents what they can and can't do. Today no one parses all eight standards. NYT, Reddit, and Stack Overflow are suing over training data; the EU AI Act now requires opt-out compliance. Building this gate from scratch is weeks of work per agent.

Maango aggregates 1,000,000+ domains × 8 AI-policy standards into one canonical answer. Your agent calls check_permission(domain, action) and gets back allowed: true/false + a structured reason. That's it.

Tools exposed

Tool What it does
check_permission(domain, action, agent_id?) Decide in one call whether an action is allowed. Returns allowed + reason code + explanation + stance + signals.
lookup_domain(domain) Summary of a domain's AI policy — stance, use-cases, bots, signals.
lookup_domain_full(domain) Full raw policy data including robots.txt, ai.txt, llms.txt, TDM-Rep, meta tags.
lookup_domain_conflicts(domain) Cross-signal conflicts (e.g. robots.txt vs ToS).
search_domains(query, stance?, limit?, offset?) Prefix search the registry with optional stance filter.
batch_check(domains[]) Compare policies across 2–25 domains side by side.
get_changelog(domain?, change_type?, limit?, offset?) Policy change history.

Reason codes returned by check_permission

  • compliant — action explicitly permitted
  • action_blocked — the specific use-case (training/search/ai_input) is blocked
  • bot_blocked — the named agent_id is on the domain's blocked-bots list
  • stance_blocks_all — domain blocks all AI access site-wide
  • no_policy — no policy on file; conservative default is deny
  • unspecified — action or use-case not addressed by the policy
  • lookup_error — registry could not be reached

Installation

Claude Desktop (remote, recommended)

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "maango": {
      "url": "https://mcp.maango.io/sse"
    }
  }
}

Restart Claude Desktop. Ask: "Check if I can scrape nytimes.com for training data."

Cursor

Settings → MCP → Add new MCP Server:

{
  "maango": {
    "url": "https://mcp.maango.io/sse"
  }
}

Cline / Zed / any MCP client

Point them at https://mcp.maango.io/sse. No auth required for the hosted endpoint.

Local development (stdio)

git clone https://github.com/maango-io/maango-mcp.git
cd maango-mcp
uv venv && source .venv/bin/activate
uv pip install -e .
cp .env.example .env          # optionally add MAANGO_API_KEY for higher rate limits
maango-mcp                    # runs with stdio transport

Then in Claude Desktop:

{
  "mcpServers": {
    "maango": {
      "command": "maango-mcp"
    }
  }
}

Self-hosting

Any platform that can run a Python HTTP service works. Quick Docker path:

docker build -t maango-mcp .
docker run -p 8000:8000 \
  -e MAANGO_API_KEY=maango_sk_xxx \
  -e MAANGO_MCP_TRANSPORT=sse \
  maango-mcp

Environment variables:

Var Default Purpose
MAANGO_MCP_TRANSPORT stdio stdio | sse | streamable-http
MAANGO_MCP_HOST 0.0.0.0 Bind address (remote transports only)
MAANGO_MCP_PORT 8000 Bind port (remote transports only)
MAANGO_API_BASE_URL https://api.maango.io Maango REST API base URL
MAANGO_API_KEY (none) Optional bearer token for higher rate limits

How it works

┌────────────────┐     MCP (sse/streamable-http)     ┌─────────────────┐
│ Claude Desktop │ ◄───────────────────────────────► │  mcp.maango.io  │
│  Cursor, …     │                                   │  (this server)  │
└────────────────┘                                   └────────┬────────┘
                                                              │ HTTPS
                                                              ▼
                                                     ┌─────────────────┐
                                                     │  api.maango.io  │
                                                     │  (REST, 1M      │
                                                     │   domains)      │
                                                     └─────────────────┘

The MCP server is a thin wrapper. The real data lives in the Maango REST API. We normalise the response into MCP-friendly tool output and handle the action → use-case mapping (e.g. "scrape" → training policy check).

Observability

The server exposes two HTTP endpoints when running on sse or streamable-http transports (not stdio — there's no port to bind):

  • GET /health — cheap liveness probe, no upstream call. Used by Docker HEALTHCHECK, nginx, and uptime monitors.
  • GET /metrics — Prometheus exposition. Tracks maango_mcp_tool_requests_total{tool,status} and maango_mcp_tool_duration_seconds{tool} (histogram).

Logs are emitted as one JSON object per stderr line with a per-tool-call req_id that propagates through the client and decision-tree. Pipe stderr to your log shipper of choice (Loki / Datadog / CloudWatch).

Development

See CONTRIBUTING.md for the full workflow. Quick start:

uv sync --extra dev
uv run pytest -q
uv run maango-mcp                                # stdio
MAANGO_MCP_TRANSPORT=sse uv run maango-mcp       # SSE on :8000

Security disclosures: see SECURITY.md.

Roadmap (not in v0.1)

  • Web Bot Auth signature verification
  • Capability-token issuance (Biscuits / Macaroons)
  • Payment-required flow via x402
  • Receipt IDs with tamper-evident Merkle proof
  • Real-time policy negotiation (Phase 3)

The roadmap is shaped by what users actually need — see Issues for the live list.

Contributing

If you find this useful:

  • ⭐ Star the repo — that's how more agents find it.
  • 🐛 Open an issue for bugs, missing domains, or anything in a tool's output that surprised you. Include the req_id from the JSON log line if you have it.
  • 💡 Have a use case the current 7 tools don't cover? File an issue with a real example — that beats abstract feature requests every time.
  • 🛠 PRs welcome — see CONTRIBUTING.md for the dev workflow and PR checklist.
  • 🔒 Security disclosures: SECURITY.md. Email instead of opening an issue.

Links

  • Main site: https://maango.io
  • API docs: https://maango.io/docs
  • Spec: https://github.com/maango-io/agent-permissions
  • Issues: https://github.com/maango-io/maango-mcp/issues
  • Changelog: CHANGELOG.md

License

MIT — see LICENSE.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured