anythingmcp

AnythingMCP is a self-hosted, open-source smart, AI-empowered MCP gateway and MCP server that turns the systems you already run into Model Context Protocol tools — REST and SOAP APIs, GraphQL, SQL & NoSQL databases, and even other MCP servers. Import a spec or point it at a database, and expose it as a custom connector to Claude, ChatGPT, Gemini, Copilot, Cursor and any MCP-compatible client. No SDK, no code changes — point, configure, connect.

It ships with 175+ ready-to-use adapters — including Deutsche Bahn, weclapp ERP, Etsy, Shopware, DHL and Sendcloud — so the most common integrations work in one click, while the visual editor and import tools (OpenAPI/Swagger, Postman, cURL, WSDL, GraphQL) let you wrap any other API or database in minutes.

What makes it smart, not just a pipe: AnythingMCP builds a per-workspace Knowledge Graph of how your connectors' data relates, serves it back to the agent over MCP so it chains tools correctly across systems, and turns how your tools are actually used into reusable AI skills. A plain gateway forwards calls; AnythingMCP gives your agents the context to use them well. (All AI features are optional and opt-in — the gateway works fully without them.)

https://github.com/user-attachments/assets/2ae92f90-7012-4c00-8836-bae5a6422ca6

• Get started in 60 seconds
• Key features
• Knowledge Graph & AI skills
• Build custom Claude connectors — no code
• Turn your API into a ChatGPT app
• Why AnythingMCP
• Pre-configured MCP connectors
• Guides, client setup & FAQ
• Community & support
• License

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Get started in 60 seconds

Requires Docker 24+, bash, openssl. On macOS, start Docker Desktop first.

git clone https://github.com/HelpCode-ai/anythingmcp.git
cd anythingmcp && ./setup.sh
# When setup finishes, open http://localhost:3000 and register
# the first user — they automatically become the admin.

The interactive setup handles everything: deployment mode, domain & HTTPS (automatic Let's Encrypt via Caddy), secrets, MCP auth mode, optional SMTP/Redis.

⚠️ Register immediately after setup. The first account to register becomes Admin. If your instance is reachable from the internet during setup, configure firewall rules or bind the UI to 127.0.0.1 until you've created the admin account.

Service	Default URL
Web UI	http://localhost:3000
MCP endpoint	http://localhost:4000/mcp
Swagger docs	http://localhost:4000/api/docs

Or one-click deploy:

   

Prefer manual setup? Copy .env.example to .env and run docker compose up -d — see the Deployment Guide.

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Key features

• 5 connector types — REST, SOAP, GraphQL, Database (PostgreSQL, MySQL, MariaDB, MSSQL, Oracle, MongoDB, SQLite), MCP-to-MCP bridge
• 6 import formats — OpenAPI/Swagger, Postman, cURL, WSDL, GraphQL introspection, custom JSON
• 175+ pre-built adapters — logistics, ERP, HR, e-commerce, payments, public data — see catalog
• Visual tool editor — map parameters to path, query, body, headers; rename and describe tools for the AI
• Dynamic MCP server — tools registered at runtime, no restart
• Knowledge Graph & AI skills — a per-workspace, PII-safe map of how your connectors' data relates, served to the agent via an MCP tool, plus reusable AI skills composed into the server's instructions (optional, opt-in)
• Full auth — OAuth2 (PKCE + Client Credentials), Bearer, API Key, Basic, WS-Security, client certificates, LOGIN_TOKEN and OAuth 1.0a handshakes
• Audit logging — every tool call logged with input, output, duration, status
• Roles & access control — tool-level whitelisting per custom role, per-user MCP API keys
• Environment variables — per-connector {{VAR}} interpolation, hidden from the AI
• Docker ready — docker compose up and you're running

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Knowledge Graph & AI skills

Beyond exposing tools, AnythingMCP can map how the data in your connectors relates — and feed that context back to the AI client so it chains tools correctly across systems.

• Knowledge Graph — a per-workspace map of entities (customers, orders, products…) and their relationships. It builds itself from tool names, parameters and the input/output of real calls; an optional AI pass infers the cross-connector links heuristics miss. It stays PII-safe: it stores entity/field names and relationship metadata, never the values.
• Build it visually — a graph editor lets you create, edit and delete entities and connections by hand, add descriptions, and curate what the AI proposed. Zoom/fit controls, connectivity-based layout and hover focus make a large graph navigable.
• Served over MCP — each server exposes a kg_how_to_obtain tool so the customer's agent can ask "how do I get this?" and receive chaining hints across connectors.
• AI skills — reusable business rules captured from how the tools are actually used (e.g. "today's revenue includes order statuses 2, 3 and 4"), reviewed by a human, then composed into the MCP server's instructions — so they guide the agent without adding any extra tool calls. Search, status tabs and pagination keep them manageable at scale; optional auto-apply for high-confidence skills and an AI "consolidate" action merges overlapping ones into fewer rules.

The AI passes (graph enrichment, skill generation, scheduled extension) work with OpenAI, OpenRouter or Anthropic and are off by default — opt-in with a global env flag and a per-workspace switch. The graph, manual editing and the MCP tool work with no LLM key at all.

➡️ Knowledge Graph & AI skills guide →

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Build custom Claude connectors — no code

Claude supports custom connectors: remote MCP servers you add once in Settings → Connectors, and that work across Claude.ai, Claude Desktop and Claude Code. AnythingMCP creates that connector from any API you already have — without writing an MCP server:

1. Import your API spec (OpenAPI/Swagger, Postman, cURL, WSDL, GraphQL introspection) or pick a pre-built adapter
2. Adjust tool names, descriptions and parameters in the visual editor — what the AI sees is up to you
3. Add the gateway URL to Claude as a custom connector (OAuth 2.0 supported out of the box)

Your credentials stay on your infrastructure (AES-256-GCM at rest), every tool call lands in the audit log, and role-based access controls which users see which tools. Step-by-step guide →

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Turn your API into a ChatGPT app

Apps in ChatGPT — what OpenAI renamed connectors to in December 2025 — are built on MCP, and AnythingMCP gives you that MCP backend without writing one. Point it at your REST, SOAP, GraphQL or database endpoint and you get a ChatGPT-ready connector: add it in ChatGPT's settings (or use it as the tool layer of an Apps SDK app) and ChatGPT can read and act on your business data.

The same connector works simultaneously in Claude, ChatGPT, Gemini, Copilot and Cursor — build once, connect everywhere. ChatGPT setup guide →

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Why AnythingMCP

AI clients speak MCP, but your systems speak REST, SOAP, GraphQL and SQL. Writing and maintaining a bespoke MCP server per system — with auth, audit and access control — takes weeks each. AnythingMCP is the no-code layer in between:

Problem	Solution
You have REST APIs but AI clients speak MCP	REST → MCP conversion with OpenAPI / Swagger import
You have legacy SOAP/WSDL services	SOAP → MCP bridge with automatic WSDL parsing
You need to query databases from AI agents	DB → MCP with auto-generated query tools (7 engines)
You want one MCP gateway for all your APIs	MCP middleware that aggregates multiple connectors
You need an MCP server for Deutsche Bahn / DHL / weclapp / …	175+ pre-built adapters — install in one click
You can't ship credentials to a SaaS gateway	Runs on your infrastructure — credentials AES-256-GCM at rest
You need auth, audit logs, and RBAC	Built-in OAuth2, audit log, and role-based access — no DIY

Typical use cases — search train schedules and live delays with Deutsche Bahn · talk to your ERP from Claude (weclapp, Xentral) · track parcels with AI ([DH