egutierrez
dbf5b45acd
- .mcp.json - bash/functions/pipelines/full_git_push.sh - python/pyproject.toml - python/uv.lock - bash/functions/infra/jupyter_mcp_serve.md - bash/functions/infra/jupyter_mcp_serve.sh - dev/issues/0166-app-to-app-dependencies-tracking.md Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
110 lines
3.7 KiB
Bash
Executable File
110 lines
3.7 KiB
Bash
Executable File
#!/usr/bin/env bash
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# jupyter_mcp_serve — arranca (o reusa) un Jupyter Lab colaborativo y lanza el
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# Jupyter MCP server enganchado a el por stdio. Pensado para ser el `command` de
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# la entrada "jupyter" en .mcp.json: garantiza que el MCP SIEMPRE tiene servidor.
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#
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# Por que existe: el MCP datalayer NO arranca jupyter, solo se conecta. Si la URL
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# apunta a un puerto sin jupyter (en esta maquina 8888 = proxy VPN gluetun), el
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# MCP nunca conecta. Este wrapper levanta su propio jupyter en un puerto propio.
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#
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# Env overrides:
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# JUPYTER_MCP_ROOT raiz de notebooks (default: raiz del repo)
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# JUPYTER_MCP_PORT puerto del jupyter gestionado (default: 8899)
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# JUPYTER_MCP_VENV venv (default: <repo>/python/.venv)
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# JUPYTER_MCP_TOKEN token (default: "" — solo escucha en 127.0.0.1)
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#
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# stdout esta RESERVADO al protocolo stdio del MCP. Todo log va a stderr + LOGFILE.
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# Nunca hacer echo a stdout aqui.
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#
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# Uso directo / test:
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# bash jupyter_mcp_serve.sh --dry-run # arranca jupyter, NO exec del MCP, loguea args
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set -euo pipefail
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DRY=0
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[ "${1:-}" = "--dry-run" ] && DRY=1
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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# raiz del repo = tres niveles arriba de bash/functions/infra/
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REPO_ROOT="$(cd "$SCRIPT_DIR/../../.." && pwd)"
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VENV="${JUPYTER_MCP_VENV:-$REPO_ROOT/python/.venv}"
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ROOT_DIR="${JUPYTER_MCP_ROOT:-$REPO_ROOT}"
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PORT="${JUPYTER_MCP_PORT:-8899}"
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HOST=127.0.0.1
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TOKEN="${JUPYTER_MCP_TOKEN:-}"
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LOGDIR="${HOME}/.fn_jupyter_mcp"
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mkdir -p "$LOGDIR"
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LOGFILE="$LOGDIR/wrapper.log"
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JLOG="$LOGDIR/jupyterlab.log"
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log(){ printf '[%s] %s\n' "$(date '+%H:%M:%S')" "$*" >>"$LOGFILE"; printf '%s\n' "$*" >&2; }
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JUPYTER="$VENV/bin/jupyter"
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MCP="$VENV/bin/jupyter-mcp-server"
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if [ ! -x "$JUPYTER" ]; then
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log "FATAL: $JUPYTER no existe. Instala: cd $REPO_ROOT/python && uv pip install --python .venv/bin/python3 jupyterlab jupyter-collaboration jupyter-mcp-server"
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exit 1
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fi
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if [ ! -x "$MCP" ]; then
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log "FATAL: $MCP no existe. Instala jupyter-mcp-server en el venv."
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exit 1
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fi
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server_up(){
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local code
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code="$(curl -s -m 3 -o /dev/null -w '%{http_code}' "http://$HOST:$PORT/api/status?token=$TOKEN" 2>/dev/null || true)"
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[ "$code" = "200" ]
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}
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if server_up; then
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log "reuso jupyter existente en $HOST:$PORT"
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else
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log "arranco jupyter colaborativo en $HOST:$PORT (root=$ROOT_DIR)"
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nohup "$JUPYTER" lab \
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--no-browser \
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--ServerApp.ip="$HOST" \
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--ServerApp.port="$PORT" \
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--ServerApp.root_dir="$ROOT_DIR" \
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--IdentityProvider.token="$TOKEN" \
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--ServerApp.disable_check_xsrf=True \
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--ServerApp.allow_origin='*' \
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>>"$JLOG" 2>&1 &
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disown 2>/dev/null || true
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# esperar hasta ~30s a que levante
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for _ in $(seq 1 60); do
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server_up && break
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sleep 0.5
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done
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if ! server_up; then
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log "FATAL: jupyter no levanto en 30s. Ver $JLOG"
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exit 1
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fi
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log "jupyter arriba"
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fi
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BASE="http://$HOST:$PORT"
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# Detectar el dialecto de CLI del MCP (cambia entre versiones de jupyter-mcp-server)
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HELP="$("$MCP" --help 2>&1 || true)"
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ARGS=(--transport stdio)
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if printf '%s' "$HELP" | grep -q -- '--document-url'; then
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ARGS+=(--document-url "$BASE" --runtime-url "$BASE")
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printf '%s' "$HELP" | grep -q -- '--document-token' && ARGS+=(--document-token "$TOKEN" --runtime-token "$TOKEN")
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elif printf '%s' "$HELP" | grep -q -- '--jupyter-url'; then
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ARGS+=(--jupyter-url "$BASE" --jupyter-token "$TOKEN")
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else
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# fallback: variables de entorno que las distintas versiones reconocen
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export DOCUMENT_URL="$BASE" RUNTIME_URL="$BASE" DOCUMENT_TOKEN="$TOKEN" RUNTIME_TOKEN="$TOKEN"
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export JUPYTER_URL="$BASE" JUPYTER_TOKEN="$TOKEN"
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fi
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log "MCP cmd: $MCP ${ARGS[*]}"
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if [ "$DRY" = "1" ]; then
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log "--dry-run: no ejecuto el MCP. Jupyter sigue corriendo en $BASE"
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exit 0
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fi
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exec "$MCP" "${ARGS[@]}"
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