Opp Repl MCP Server

MCP server for AI assistants

opp_repl ships an MCP server that lets an AI assistant execute Python in the live IPython session and browse auto-generated documentation resources. It speaks two transports:

1. Unix domain socket (--mcp-socket) — recommended for local use. No token; access is controlled by 0600 file permissions. AI tools connect through the opp_repl_mcp_bridge stdio bridge, which most MCP clients launch for you.
2. TCP / streamable-HTTP (--mcp-port) — for remote clients or ones that only speak HTTP. Requires a bearer token (or an explicit bypass) outside opp_sandbox.

Upstream reference: https://github.com/omnetpp/opp_repl/blob/main/doc/mcp_server.md

Requirements

pip install "opp_repl[mcp]"

The MCP server is OFF by default

--mcp-port defaults to 0 (disabled) and --mcp-socket is unset. You must explicitly enable one of them; they are mutually exclusive (passing both is an error).

Transport 1 — Unix domain socket (recommended, local)

Start the REPL with a socket:

opp_repl --mcp-socket --load "etc/*.opp"          # default per-user path
opp_repl --mcp-socket /tmp/opp_repl/mcp.sock ...  # explicit path

• Default path: $XDG_RUNTIME_DIR/opp_repl/mcp.sock, falling back to /tmp/opp_repl-<uid>/mcp.sock.
• No bearer token — the socket's 0600 permissions are the access control. --mcp-token-hash / --mcp-bypass-token-hash-check are rejected with --mcp-socket.

AI clients reach the socket through the stdio bridge:

opp_repl_mcp_bridge                       # default socket path
opp_repl_mcp_bridge --mcp-socket <path>   # explicit path

opp_repl_mcp_bridge --help prints the resolved default path and ready-to-paste client config snippets.

Transport 2 — TCP / streamable-HTTP (remote, legacy)

# Generate a token hash (Linux: sha256sum, macOS: shasum -a 256)
TOKEN=$(python3 -c 'import secrets; print(secrets.token_urlsafe(32))')
HASH=$(printf '%s' "$TOKEN" | shasum -a 256 | cut -d' ' -f1)

opp_repl --mcp-port 9966 --mcp-token-hash "$HASH" --load "etc/*.opp"

• Endpoint: http://127.0.0.1:9966/mcp (stateless streamable HTTP).
• Clients send Authorization: Bearer <TOKEN> (the raw token, not the hash).
• Outside opp_sandbox, starting TCP mode without --mcp-token-hash AND without --mcp-bypass-token-hash-check raises an error.
• --mcp-bypass-token-hash-check disables auth (trusted networks only). Inside opp_sandbox the auth requirement is waived automatically — see opp-repl-sandbox.

Tool: execute_python(code: str) -> str

The only MCP tool. Runs code in the SAME IPython namespace the interactive user sees — all opp_repl functions, every loaded project's {name}_project variable, and any prior session state.

Returns the repr of the last expression (if any) followed by captured stdout/stderr/logging; output is also streamed to the client as log notifications and printed to the REPL console live. Per the tool's own guidance: read the documentation resources first, don't guess signatures, and don't print() just to view a value (the last expression's repr is returned automatically).

Resources

Names can be fully qualified (opp_repl.simulation.workspace.SimulationWorkspace) or short (SimulationWorkspace).

Client configuration

Claude Code (recommended)

claude mcp add --transport stdio opp_repl -- opp_repl_mcp_bridge

…or add to .mcp.json (project) / ~/.claude.json (user):

{
  "mcpServers": {
    "opp_repl": { "type": "stdio", "command": "opp_repl_mcp_bridge" }
  }
}

Windsurf (~/.codeium/windsurf/mcp_config.json)

{ "mcpServers": { "opp_repl": { "command": "opp_repl_mcp_bridge" } } }

VS Code / Cursor (.vscode/mcp.json)

{ "servers": { "opp_repl": { "type": "stdio", "command": "opp_repl_mcp_bridge" } } }

TCP fallback (any HTTP MCP client)

{
  "mcpServers": {
    "opp_repl": {
      "url": "http://localhost:9966/mcp",
      "headers": { "Authorization": "Bearer <your_token>" }
    }
  }
}

Pass --mcp-socket <path> in the bridge's args (or opp_repl_mcp_bridge --mcp-socket <path>) when the REPL uses a non-default socket path.

Recommended discovery flow for an AI agent

1. opp-repl://guides → which task-level guides exist.
2. opp-repl://guide/{topic} → concrete examples.
3. opp-repl://packages → find the relevant sub-package.
4. opp-repl://package/{name} → compact API overview.
5. opp-repl://class/... / opp-repl://function/... → full signatures.
6. execute_python(...) with the chosen approach.

Pitfalls

• The session is LIVE and STATEFUL — state persists across execute_python calls. Two agents on one server stomp on each other; run one REPL per agent, or share deliberately (see opp-repl-shared-terminal).
• execute_python runs arbitrary code as you. Treat the endpoint as privileged; never expose TCP mode on a public network unauthenticated. For isolation, run inside opp_sandbox.
• In CI / parallel test runs, leave the MCP server OFF (the default) — don't pass --mcp-port / --mcp-socket.
• Closing the MCP client does NOT reset the REPL; the kernel keeps its state until the process exits.

See also

• opp-repl-shared-terminal — one live REPL driven by a human (tmux) AND an AI (MCP) at once.
• opp-repl-sandbox — run the MCP server under bubblewrap isolation.
• opp-repl-repl-usage — driving the same kernel from a terminal.
• opp-repl-ai-workflows — end-to-end recipes for AI integration.
• opp-repl-overview — map of which skills to load when.