EasyDeploy AI MCP (easydeploy-ai-mcp)

A Model Context Protocol (MCP) server that exposes the [EasyDeploy](https://easydeploy.ai) public REST API as tools for Claude, Cursor, Claude Code, and other MCP clients.

PyPI package name: easydeploy-ai-mcp · Import package: easydeploy_ai_mcp

Contents

• Connect with Claude
• What you get
• Requirements
• Install
• Environment variables
• Local MCP (stdio)
• Remote MCP (HTTP)
• Documentation
• REST API reference
• Releases and API compatibility
• Development
• Security
• License

Connect with Claude

Hosted connector (claude.ai or Claude Desktop)

We host the MCP endpoint. You add it once inside Claude; after you connect and sign in, new chats can use EasyDeploy like any other enabled connector.

1. Open Claude in the browser or open Claude Desktop.
2. Open Settings, then Connectors, and choose Add custom connector.
3. Enter exactly:

• Name: EasyDeploy AI
• Remote MCP server URL: https://mcp.easydeploy.ai/mcp

1. Save the connector so EasyDeploy appears in your list of connectors.
2. Open the EasyDeploy connector entry and choose Connect, then finish sign-in in your browser. That step authorizes Claude to use your EasyDeploy account.

Connect and sign in (inside Claude): After the custom connector is set up, Claude shows an EasyDeploy card with the MCP URL and a Connect button. Use that flow to sign in. You do not paste an API key into Claude. Access stays tied to the EasyDeploy profile you authenticate in the browser.

Claude Desktop and file uploads: File uploads and some tool calls reach EasyDeploy over the network. On Desktop, Claude blocks outbound traffic unless you allow the domains it should call. Open Settings → Capabilities, turn on Allow network egress, and under the domain allowlist add this entry exactly (including the leading *.):

*.execute-api.us-east-1.amazonaws.com

The domain allowlist UI is available on paid Claude plans.

More detail and variants (for example a URL from your own deployment) are in docs/claude-getting-started.md.

Local MCP on your computer (stdio)

Run the MCP server on your own machine using your EasyDeploy API key. Nothing is exposed to the internet.

1. Install

pip install easydeploy-ai-mcp

2. Add to Claude Desktop config

Edit (or create) the Claude Desktop config file:

Merge the following into the root of that JSON (keep any existing keys):

{
  "mcpServers": {
    "EasyDeploy AI": {
      "command": "easydeploy-ai-mcp-stdio",
      "env": {
        "EDA_API_KEY": "eda_live_YOUR_KEY"
      }
    }
  }
}

Replace eda_live_YOUR_KEY with your key from Account → API Keys in the EasyDeploy dashboard. Use the full path to easydeploy-ai-mcp-stdio (run which easydeploy-ai-mcp-stdio to find it) if Claude cannot locate it on your PATH.

3. Restart Claude Desktop

Fully quit and reopen the app. EasyDeploy AI will appear in your MCP servers.

For self-hosting on Docker or a cloud provider, see Remote MCP (HTTP).

What you get

• 24 tools covering projects, datasets (including upload flow), model versions, training jobs, predictions, and account status.
• stdio transport for local clients, or HTTP with Streamable MCP on /mcp and GET /healthz for load balancers.
• Hardening: HTTPS-only calls to the EasyDeploy API; optional MCP_SERVICE_TOKEN for the HTTP MCP surface; response fields trimmed where appropriate for agents.

For production and SOC 2–sensitive setups, prefer self-hosting so your data stays within your own infrastructure. The hosted connector at https://mcp.easydeploy.ai/mcp is fine for most users.

Requirements

• Python 3.10+
• An EasyDeploy API key from the dashboard (Account → API Keys). The client uses the production EasyDeploy API host by default.

Install

From PyPI (after first release)

pip install easydeploy-ai-mcp

From source

git clone https://github.com/easydeploy-ai/easydeploy-ai-mcp.git
cd easydeploy-ai-mcp
pip install -e ".[dev]"   # includes pytest
# or minimal runtime only:
pip install .

Environment variables

Local MCP (stdio)

Use when the client starts the server as a subprocess (Claude Desktop, Cursor, etc.).

export EDA_API_KEY="eda_live_..."
easydeploy-ai-mcp-stdio

Or: python -m easydeploy_ai_mcp

Example config snippet:

{
  "mcpServers": {
    "easydeploy-ai": {
      "command": "easydeploy-ai-mcp-stdio",
      "env": {
        "EDA_API_KEY": "eda_live_..."
      }
    }
  }
}

Remote MCP (HTTP)

Serves Streamable HTTP via FastMCP on `/mcp` (confirm with your pinned FastMCP 3.x version). Health checks: GET /healthz.

export EDA_API_KEY="eda_live_..."
easydeploy-ai-mcp-http

Or: uvicorn easydeploy_ai_mcp.http_main:app --host 0.0.0.0 --port 8080

If you embed mcp.http_app() in another ASGI app, pass through `lifespan` from the FastMCP HTTP app (FastMCP ASGI); easydeploy_ai_mcp.http_main already does this for uvicorn.

Auth modes

Pick exactly one (setting both EDA_OAUTH_ENABLED and MCP_SERVICE_TOKEN raises at import):

• OAuth 2.0 resource server (multi-tenant): set EDA_OAUTH_ENABLED=1 plus

EDA_COGNITO_USER_POOL_ID and EDA_COGNITO_CLIENT_ID. Install the optional extra: pip install easydeploy-ai-mcp[oauth]. The server validates incoming Cognito access JWTs locally against the Cognito JWKS (issuer, signature, exp, token_use=='access', client_id) and forwards the token to the EasyDeploy API. EasyDeploy API keys (prefix eda_live_) are accepted in the same Authorization: Bearer header and forwarded as-is — the API is the source of truth for revocation. RFC 9728 metadata is published at /.well-known/oauth-protected-resource; RFC 8414 proxy metadata includes `registration_endpoint`, and `POST /oauth/register` returns the static Cognito MCP `EDA_COGNITO_CLIENT_ID` (RFC 7591-style, public client). 401 responses include WWW-Authenticate: Bearer … so MCP clients can discover the auth server. Note: Cognito access tokens carry client_id, not aud; do not configure an audience.

• Shared-secret gate (legacy single-tenant): set MCP_SERVICE_TOKEN. All

outbound API calls use the static EDA_API_KEY.

• No auth: development only.

Docker — run locally (same image you deploy to ECS/Fargate; includes easydeploy-ai-mcp[oauth]):

# Convenience: build + run (reads .env in the repo root if present)
./scripts/run_mcp_docker_local.sh

# Explicit env vars (no .env)
./scripts/run_mcp_docker_local.sh -e EDA_API_KEY="eda_live_..."

# Different host port
PORT=9000 ./scripts/run_mcp_docker_local.sh

Host on AWS (Fargate + ALB): build and push this repo’s Dockerfile to a container registry, then deploy behind an HTTPS load balancer. Set the env vars listed above on the task/container.

Manual equivalent:

docker build -t easydeploy-ai-mcp .
docker run --rm -p 8080:8080 \
  -e EDA_API_KEY="eda_live_..." \
  easydeploy-ai-mcp

Documentation

• [docs/claude-getting-started.md](docs/claude-getting-started.md) — EasyDeploy + Claude: Connectors or local Desktop config JSON
• [docs/claude.md](docs/claude.md) — Claude Connectors vs Claude Code, transports, headers

REST API reference

This MCP server is a thin client over the EasyDeploy public REST API. Endpoint behavior, request bodies, and response shapes are defined by EasyDeploy (dashboard, product help, and official API materials at easydeploy.ai). This repo does not duplicate the full OpenAPI spec; it maps those operations to MCP tools.

Releases and API compatibility

This repository is the open-source home of the EasyDeploy MCP server. New releases track the EasyDeploy public REST API as documented for customers (dashboard and official API materials). If the API adds or changes endpoints, expect corresponding updates here. Contributors should follow CONTRIBUTING.md when changing tools or client behavior.

Development

pip install -e ".[dev]"
pytest

Optional — real Cognito JWT against the HTTP app (live JWKS, no mocks): set EDA_INTEGRATION_COGNITO_ACCESS_TOKEN plus the same EDA_COGNITO_* vars you use for OAuth mode, then run pytest tests/test_cognito_jwt_integration.py -v. See the docstring in that file.

See CONTRIBUTING.md for pull requests and reporting issues.

Security

See SECURITY.md for vulnerability reporting and deployment notes.

License

MIT — see LICENSE.