Open Enthrium

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What is OE MCP Server?

OE MCP Server is a standalone binary that implements the Model Context Protocol (MCP) and exposes your enterprise data sources as tools that AI apps can use directly.

Connect Claude Code, Cursor, Windsurf, Codex, Claude Desktop, or VS Code to your PostgreSQL database, local filesystem, GitHub, Slack, Google Drive, SSH servers, and more — without writing any integration code.

• No code. Define connectors in a single JSON file.
• 45+ connector categories. Enterprise systems supported out of the box.
• Two transport modes. --stdio for Claude Code, Cursor, Windsurf, Codex, and Claude Desktop (launched as a child process); --serve for cloud deployments or sharing one server across a team.
• Persistent memory. Built-in memory_set / memory_get / memory_list / memory_delete tools — context survives across sessions.
• Action log. Built-in log_list / log_clear tools — every connector call is automatically recorded with timestamp, connector, tool, input, and result.
• Run AI agents. run_agent executes any OE Runtime YAML agent directly from Claude Code, Cursor, Windsurf, or any MCP-enabled AI chat — no terminal required.
• Agent chains. Chain agents together in YAML — auto chains fire in sequence and return nested results; manual chains pause for human approval via approve_chain; works in Claude Code, Cursor, Telegram, or any MCP client.
• Self-hosted. Runs on your own machine. No cloud dependency. Own your data.

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Setup in 3 Steps

1. Create oe-mcp.json — define your connectors (databases, files, APIs, and more).
2. Register OE MCP — add to your AI app's MCP config using --stdio (Claude Code, Cursor, Windsurf, Codex, Claude Desktop, VS Code), or start with --serve for cloud or team deployments.
3. Test — ask Claude "What connectors do you have access to?" and try saving a memory.

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Quick Start via npm (Recommended)

No binary download needed — npx handles everything automatically.

Add to your AI app's MCP config (Claude Code, Cursor, Windsurf, Codex, Claude Desktop, VS Code)

macOS / Linux:

{
  "mcpServers": {
    "oe-mcp": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@openenthrium/oe-mcp", "--stdio", "/path/to/oe-mcp.json"]
    }
  }
}

Windows:

{
  "mcpServers": {
    "oe-mcp": {
      "type": "stdio",
      "command": "npx.cmd",
      "args": ["-y", "@openenthrium/oe-mcp", "--stdio", "C:\\path\\to\\oe-mcp.json"]
    }
  }
}

Note: -y tells npx to skip the install confirmation prompt. Without it, npx waits for keyboard input and the MCP connection never opens.

Reload your AI app — done.

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Download (Standalone Binary)

Prefer a standalone binary? Download for your platform:

Platform	Binary
Windows	oe-mcp-win.exe
Linux	oe-mcp-linux
macOS	oe-mcp-macos
Sample configs	oe-mcp-samples.zip — ready-to-use oe-mcp.json for common connectors

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Quick Start (Binary)

1. Download the binary for your OS

# Linux / macOS — make executable
chmod +x oe-mcp-linux

2. Create your config file (oe-mcp.json)

{
  "connectors": [
    {
      "name": "my-postgres",
      "type": "postgresql",
      "host": "localhost",
      "port": 5432,
      "database": "mydb",
      "user": "postgres",
      "password": "secret"
    },
    {
      "name": "my-codebase",
      "type": "filesystem",
      "basePath": "/home/user/projects/myapp"
    }
  ],
  "memory": [
    { "key": "project_context", "value": "This is our main application database." }
  ]
}

3. Add to your AI app's MCP config (Claude Code, Cursor, Windsurf, Codex, Claude Desktop, VS Code)

{
  "mcpServers": {
    "oe-mcp": {
      "type": "stdio",
      "command": "/path/to/oe-mcp-win.exe",
      "args": ["--stdio", "/path/to/oe-mcp.json"]
    }
  }
}

Reload your AI app — the MCP tools appear automatically.

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Test Your Connection and Memory

Test Connectors

Once connected, ask Claude in plain language:

"What connectors do you have access to?"

Claude will list every connected tool with its available actions. Example response:

Connector	Tools
my-postgres	query
my-github	list_files, read_file, create_issue, get_issue, search_issues
my-slack	list_channels, post_message, search_messages
my-codebase	list_dir, read_file, write_file, search_files

You can also run /mcp in Claude Code to see the server status and total tool count.

Test Memory

OE MCP has built-in persistent memory that survives restarts. Use plain language or direct tool calls:

Save a memory:

"Remember that our production database host is prod-db.company.com"

Claude calls memory_set with key = main_db_host, value = prod-db.company.com.

Retrieve a memory:

"What is our production database host?"

Claude calls memory_get with key = main_db_host and returns the stored value.

List all memories:

"What do you remember about our project?"

Claude calls memory_list and returns all stored key-value pairs.

Delete a memory:

"Forget the production database host."

Claude calls memory_delete with key = main_db_host to remove it.

Memory is stored in oe-mcp-memory.json next to your oe-mcp.json and persists across sessions and restarts.

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HTTP Mode (Cloud / Team Deployments)

Use --serve when you want to run OE MCP as a standalone HTTP server — for cloud deployments or sharing one server across a team.

# Start the MCP server
oe-mcp-win.exe --serve --port 4040 oe-mcp.json
# OE MCP Server listening on http://localhost:4040/mcp

In Cursor settings → MCP → Add server:

http://localhost:4040/mcp

In Claude Desktop claude_desktop_config.json:

{
  "mcpServers": {
    "oe-mcp": {
      "url": "http://localhost:4040/mcp"
    }
  }
}

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Cloud Deployment (MCP as a Service)

Deploy oe-mcp-linux to any cloud server — AWS EC2, fly.io, Railway, DigitalOcean — and multiple developers connect to it via URL. No binary needed on each developer machine.

# On your cloud server
./oe-mcp-linux --serve --port 4040 /etc/oe-mcp/oe-mcp.json

Each developer adds to their Cursor / Windsurf:

http://your-server.com:4040/mcp

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Config File Reference (oe-mcp.json)

{
  "connectors": [
    {
      "name": "<display-name>",
      "type": "<connection-type>",
      "...": "connector-specific credentials"
    }
  ],
  "memory": [
    { "key": "<key>", "value": "<value>" }
  ]
}

Example — Multiple Connectors

{
  "connectors": [
    { "name": "my-postgres",  "type": "postgresql",     "host": "db.company.com", "port": 5432, "database": "production", "user": "readonly", "password": "secret" },
    { "name": "my-mysql",     "type": "mysql",          "host": "localhost",       "port": 3306, "database": "mydb",       "user": "root",     "password": "secret" },
    { "name": "my-mongo",     "type": "mongodb",        "uri": "mongodb://localhost:27017",       "database": "mydb" },
    { "name": "my-redis",     "type": "redis",          "host": "localhost",       "port": 6379 },
    { "name": "my-elastic",   "type": "elasticsearch",  "node": "https://localhost:9200",         "apiKey": "xxxxxxxxxxxx" },
    { "name": "my-s3",        "type": "s3",             "accessKeyId": "AKIAXXXXXXXX",            "secretAccessKey": "xxxxxxxxxxxx", "region": "us-east-1", "bucket": "my-bucket" },
    { "name": "my-gdrive",    "type": "gdrive",         "clientId": "xxxx.apps.googleusercontent.com", "clientSecret": "xxxx", "refreshToken": "xxxx" },
    { "name": "my-github",    "type": "github",         "repoUrl": "https://github.com/your-org/your-repo", "personalAccessToken": "ghp_xxxxxxxxxxxx" },
    { "name": "my-jira",      "type": "jira",           "host": "https://company.atlassian.net",  "email": "you@company.com", "apiToken": "xxxx" },
    { "name": "my-slack",     "type": "slack",          "botToken": "xoxb-xxxxxxxxxxxx" },
    { "name": "my-gmail",     "type": "gmail",          "clientId": "xxxx.apps.googleusercontent.com", "clientSecret": "xxxx", "refreshToken": "xxxx" },
    { "name": "my-smtp",      "type": "smtp",           "host": "smtp.company.com", "port": 587,  "user": "you@company.com", "password": "secret" },
    { "name": "my-server",    "type": "ssh",            "host": "server.company.com", "port": 22, "username": "ubuntu", "privateKey": "-----BEGIN OPENSSH PRIVATE KEY-----\nYOUR_PRIVATE_KEY_CONTENT\n-----END OPENSSH PRIVATE KEY-----" },
    { "name": "my-codebase",  "type": "filesystem",    "basePath": "/home/user/projects" },
    { "name": "my-api",       "type": "rest-api",       "baseUrl": "https://api.company.com",     "headers": { "Authorization": "Bearer xxxx" } },
    { "name": "my-hubspot",   "type": "hubspot",        "accessToken": "pat-xxxxxxxxxxxx" },
    { "name": "my-kafka",     "type": "kafka",          "brokers": ["localhost:9092"] }
  ],
  "memory": [
    { "key": "team",        "value": "Platform Engineering" },
    { "key": "environment", "value": "production" }
  ]
}

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Built-in Tools

Connector Tools

Each connector exposes a set of tools prefixed with the connector name. Examples:

Connector	Tools
postgresql / mysql / mongodb	query — run SQL or aggregation queries
filesystem	list_dir, read_file, write_file, append_file, delete_file, make_dir, file_info, search_files
github	list_repos, get_file, create_issue, list_issues, list_prs, get_pr, search_code
slack	list_channels, post_message, get_messages, get_thread
ssh	execute_command, upload_file, download_file, list_files
gdrive	list_files, get_file, create_file, update_file, search_files
rest-api	request — any HTTP method against any endpoint

Memory Tools

Built-in memory tools available in every session:

Tool	Description
memory_set	Store a key-value pair that persists across sessions
memory_get	Retrieve a stored value by key
memory_list	List all stored key-value pairs
memory_delete	Remove a stored key

Memory is stored in oe-mcp-memory.json next to your oe-mcp.json and survives restarts.

Example usage:

"Remember that our main database is on prod-db.company.com" → Claude calls memory_set with key main_db_host and value prod-db.company.com

Action Log Tools

Built-in log tools that record every connector tool call:

Tool	Description
log_list	List recent connector action log entries (newest first, supports limit param)
log_clear	Clear all entries from the action log

Every connector tool call is automatically appended to oe-mcp-log.json next to your oe-mcp.json with timestamp, connector name, tool, input, and result. Memory and log tool calls are excluded.

Example usage:

"Show me the action log" → Claude calls log_list and returns recent connector activity

Example log entry:

{
  "ts": "2026-08-08T04:59:33.289Z",
  "connector": "my-postgres",
  "tool": "query",
  "input": { "sql": "SELECT * FROM users LIMIT 10" },
  "result": "ok"
}

Agent Runner Tools

OE MCP can run OE Runtime YAML agents directly from Claude Code, Cursor, Windsurf, Codex, or any MCP-compatible AI app — no terminal required. Agents can chain to other agents, with auto or manual approval.

Tool	Description
run_agent	Run an OE Runtime YAML agent. Returns output, auto-chain results, and any pending manual chains.
list_pending_chains	List all manual chains currently waiting for approval — shows chain_id, next agent, and output preview.
approve_chain	Approve or reject a pending manual chain by chain_id. Approved chains run immediately and return their full output.

run_agent parameters:

Parameter	Type	Required	Description
file	string	✅	Absolute path to the agent.yaml file
params	object	❌	Key-value pairs substituted into the agent prompt via {{key}}
input	string	❌	Optional initial message or context passed to the agent

Config auto-detection: OE MCP looks for oe-config.json in the same directory as agent.yaml. If found, it uses that config. Otherwise it falls back to oe-mcp.json.

Example — run an agent:

"Run my security monitor at /agents/security-monitor.yaml" → Claude calls run_agent → output returned + any pending chains listed

Example — manual chain approval:

"Approve the chain" → Claude calls approve_chain with the chain_id from the previous response → chained agent runs → output returned

Agent chain YAML syntax:

chains:
  - next_agent: ./followup.yaml     # relative path from this agent file
    trigger_type: auto              # fires immediately after this agent completes

  - next_agent: ./notify.yaml
    trigger_type: manual            # pauses — Claude asks you before running

Requires OE Runtime config. The agent directory must have a valid oe-config.json with llm and connectors configured. See OE Runtime for agent authoring docs.

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Binary vs Node.js Mode

The standalone binary works for all connector categories except Oracle, MSSQL, SQLite, and Snowflake — these use native C++ addons that cannot be bundled into a single executable.

If you need any of these four, run with Node.js instead:

git clone https://github.com/enthrium/open-enthrium-ai-mcp-server.git
cd open-enthrium-ai-mcp-server/server
yarn install
# stdio mode (Claude Code, Cursor, Windsurf, Codex, Claude Desktop, VS Code)
node mcp/index.js --stdio /path/to/oe-mcp.json
# serve mode (cloud/team deployments)
node mcp/index.js --serve --port 4040 /path/to/oe-mcp.json

All other connectors (PostgreSQL, MySQL, MongoDB, Redis, S3, Slack, GitHub, REST API, SSH, filesystem, etc.) work directly with the binary — no Node.js required.

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Connector Catalog

Connectors across 45+ categories:

Category	Examples
SQL Databases	PostgreSQL, MySQL, MSSQL, Oracle, SQLite, Snowflake, BigQuery, Redshift
NoSQL / Cache	MongoDB, Redis, Elasticsearch, DynamoDB, Cassandra
Object Storage	AWS S3, GCS, Azure Blob, MinIO, Cloudflare R2
Cloud Drives	Google Drive, OneDrive, Dropbox, Box
Filesystem	Local directories — list, read, write, search
Email	Gmail, Outlook, Zoho Mail, SMTP
Team Messaging	Slack, Microsoft Teams, Discord, Telegram
CRM / Productivity	HubSpot, Salesforce, Notion, Airtable
Issue Tracking	GitHub, Jira, GitLab, Linear
REST API	Any HTTP/REST endpoint
GraphQL	Any GraphQL endpoint
SSH / SFTP	Remote command execution, file transfer
Message Queues	Kafka, AWS SQS, Google Pub/Sub, RabbitMQ
Search	Perplexity, Google Search, Bing
LDAP / Directory	Active Directory, OpenLDAP
OCR / Vision	Azure Vision, Google Vision, AWS Textract
Image Generation	OpenAI, FLUX, Stable Diffusion
Speech & Audio	ElevenLabs, OpenAI TTS, Azure Speech
Web3 / Blockchain	Ethereum, Polygon, Solana
Helpdesk	Zendesk, Freshdesk, ServiceNow
+ more	Healthcare (FHIR), ERP (SAP), Marketing, Analytics, ...

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Sample Configs

Download oe-mcp-samples.zip for ready-to-use configs:

postgres · mysql · mongodb · github · slack · gdrive · ssh · filesystem · oracle · salesforce · servicenow · telegram · notion · confluence · graphql · zoho-mail · sftp · dropbox · multi-connector

Each sample includes the complete oe-mcp.json with setup instructions in comments.

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Transport Modes

Mode	Flag	Best for
stdio	--stdio	Claude Code, Cursor, Windsurf, Codex, Claude Desktop — binary launched as child process by the AI app
HTTP	--serve	Cloud deployments, multiple developers sharing one server

Both modes are supported in the same binary — just pass the appropriate flag.

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Part of Open Enthrium

OE MCP Server is part of the Open Enthrium platform.

⚡ Agent Runtime	open-enthrium-ai-agent-runtime — run YAML agents as CLI or HTTP server
🖥️ Platform	open-enthrium-ai-platform — full web app with workspaces, RAG, Agent Builder, DLP
🌐 Website	openenthrium.com

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Contributing

→ See CONTRIBUTING.md for how to add sample configs and connector adapters.

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License

Apache-2.0 — free to use, modify, and deploy for any purpose, including commercial use. No usage limits. No telemetry. No call-home.

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