test with docker

Author: a-s-g93

.github/workflows/pr-mcp-neo4j-data-modeling.yml

@@ -0,0 +1,45 @@
+name: MCP Neo4j Cypher Tests
+
+on:
+  push:
+    branches: [ main, master ]
+    paths:
+      - 'servers/mcp-neo4j-data-modeling/**'
+  pull_request:
+    branches: [ main, master ]
+    paths:
+      - 'servers/mcp-neo4j-data-modeling/**'
+  workflow_dispatch:  # Allows manual triggering of the workflow
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    
+    - name: Set up Python 3.12
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.12'
+    
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.cargo/bin" >> $GITHUB_PATH
+    
+    - name: Install dependencies
+      run: |
+        cd servers/mcp-neo4j-data-modeling
+        uv venv
+        uv pip install -e ".[dev]"
+
+    - name: Check format and linting
+      run: |
+        cd servers/mcp-neo4j-data-modeling
+        make format
+    
+    - name: Run tests
+      run: |
+        cd servers/mcp-neo4j-data-modeling
+        make test

.github/workflows/publish-data-modeling.yml

@@ -0,0 +1,79 @@
+# This workflow will upload a Python Package to PyPI when a release is created
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Publish Neo4j MCP Data Modeling Package
+
+on:
+  push:
+    tags:
+      - mcp-neo4j-data-modeling-v*
+  workflow_dispatch:  # Allows manual triggering of the workflow
+
+permissions:
+  contents: read
+
+jobs:
+  release-build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+
+      - name: Build release distributions
+        run: |
+          cd servers/mcp-neo4j-data-modeling/
+          python -m pip install build
+          python -m build
+
+      - name: Upload distributions
+        uses: actions/upload-artifact@v4
+        with:
+          name: release-dists
+          path: servers/mcp-neo4j-data-modeling/dist/
+
+  pypi-publish:
+    runs-on: ubuntu-latest
+    needs:
+      - release-build
+    permissions:
+      # IMPORTANT: this permission is mandatory for trusted publishing
+      id-token: write
+
+    # Dedicated environments with protections for publishing are strongly recommended.
+    # For more information, see: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment#deployment-protection-rules
+    environment:
+      name: pypi
+      url: https://pypi.org/project/mcp-neo4j-data-modeling/
+
+    steps:
+
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+
+      - name: "Set up Python"
+        uses: actions/setup-python@v5
+        with:
+          python-version-file: "servers/mcp-neo4j-data-modeling/pyproject.toml"
+
+      - name: Build release distributions
+        run: |
+          cd servers/mcp-neo4j-data-modeling/
+          uv build
+
+      - name: Publish release
+        env:
+          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
+        run: |
+          cd servers/mcp-neo4j-data-modeling/
+          uv publish

servers/mcp-neo4j-data-modeling/Dockerfile

@@ -1,22 +1,22 @@
-FROM python:3.11-slim
+FROM python:3.12-slim
 
 # Set working directory
 WORKDIR /app
 
 # Install build dependencies
-RUN pip install --no-cache-dir hatchling
+RUN pip install uv
 
+# Copy dependency files first
 COPY pyproject.toml /app/
-
-# Install dependencies
-RUN pip install --no-cache-dir neo4j>=5.26.0 mcp>=1.6.0
-
+COPY uv.lock /app/
 
 # Copy the source code
 COPY src/ /app/src/
 COPY README.md /app/
 
-RUN pip install --no-cache-dir -e .
+# Install the package
+RUN uv sync
+
 
 # Command to run the server using the package entry point
-CMD ["sh", "-c", "mcp-neo4j-data-modeling"]
+CMD ["sh", "-c", "uv run mcp-neo4j-data-modeling --transport ${MCP_TRANSPORT}"]

servers/mcp-neo4j-data-modeling/Makefile

@@ -3,11 +3,15 @@
 .PHONY: format test clean
 
 format:
-	uv run ruff format .
 	uv run ruff check --select I . --fix
+	uv run ruff check --fix .
+	uv run ruff format .
 
 test:
-	uv run pytest tests/ -vv
+	uv run pytest tests/ -s 
+
+inspector:
+	npx @modelcontextprotocol/inspector uv --directory src/mcp_neo4j_data_modeling run mcp-neo4j-data-modeling
 
 
 clean:

servers/mcp-neo4j-data-modeling/claude_desktop_config.json

@@ -0,0 +1,16 @@
+{
+  "mcpServers": {
+    "neo4j-data-modeling": {
+      "command": "docker",
+      "args": [
+        "run",
+        "--rm",
+        "-i",
+        "--network=host",
+        "-v", "${PWD}:/app",
+        "-e", "MCP_TRANSPORT=stdio",
+        "$(docker build -q -f ${PWD}/Dockerfile ${PWD})"
+      ]
+    }
+  }
+} 

servers/mcp-neo4j-data-modeling/inspector.sh

@@ -6,7 +6,6 @@ readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
     "mcp[cli]>=1.6.0",
-    "neo4j>=5.26.0",
     "neo4j-viz>=0.3.1",
     "pydantic>=2.10.1",
 ]

servers/mcp-neo4j-data-modeling/pyproject.toml

@@ -1,26 +1,17 @@
 import argparse
 import asyncio
-import os
 
 from . import server
 
 
 def main():
     """Main entry point for the package."""
-    parser = argparse.ArgumentParser(description="Neo4j data Modeling MCP Server")
-    # parser.add_argument("--db-url", default=None, help="Neo4j connection URL")
-    # parser.add_argument("--username", default=None, help="Neo4j username")
-    # parser.add_argument("--password", default=None, help="Neo4j password")
-    # parser.add_argument("--database", default=None, help="Neo4j database name")
+    parser = argparse.ArgumentParser(description="Neo4j Data Modeling MCP Server")
     parser.add_argument("--transport", default="stdio", help="Transport type")
 
     args = parser.parse_args()
     asyncio.run(
         server.main(
-            # args.db_url or os.getenv("NEO4J_URL") or os.getenv("NEO4J_URI", "bolt://localhost:7687"),
-            # args.username or os.getenv("NEO4J_USERNAME", "neo4j"),
-            # args.password or os.getenv("NEO4J_PASSWORD", "password"),
-            # args.database or os.getenv("NEO4J_DATABASE", "neo4j"),
             args.transport,
         )
     )

servers/mcp-neo4j-data-modeling/src/mcp_neo4j_data_modeling/__init__.py

@@ -33,7 +33,7 @@ class Property(BaseModel):
     "A Neo4j Property."
 
     name: str = Field(description="The name of the property. Should be in camelCase.")
-    type: str = Field(default="STRING", description="The Neo4j type of the property")
+    type: str = Field(default="STRING", description="The Neo4j type of the property. Should be all caps.")
     source: PropertySource | None = Field(
         default=None, description="The source of the property, if known."
     )
@@ -84,7 +84,7 @@ def to_arrows(self, is_key: bool = False) -> dict[str, Any]:
 class Node(BaseModel):
     "A Neo4j Node."
 
-    label: str = Field(description="The label of the node. Should be in PascalCase.")
+    label: str = Field(description="The label of the node. Should be in PascalCase.", min_length=1)
     key_property: Property = Field(description="The key property of the node")
     properties: list[Property] = Field(
         default_factory=list, description="The properties of the node"
@@ -189,7 +189,7 @@ class Relationship(BaseModel):
     "A Neo4j Relationship."
 
     type: str = Field(
-        description="The type of the relationship. Should be in SCREAMING_SNAKE_CASE."
+        description="The type of the relationship. Should be in SCREAMING_SNAKE_CASE.", min_length=1
     )
     start_node_label: str = Field(description="The label of the start node")
     end_node_label: str = Field(description="The label of the end node")

servers/mcp-neo4j-data-modeling/src/mcp_neo4j_data_modeling/data_model.py

@@ -4,6 +4,7 @@
 from typing import Any, Literal
 
 from mcp.server.fastmcp import FastMCP
+from mcp import types as mcp_types
 from pydantic import ValidationError
 
 from .data_model import (
@@ -55,44 +56,47 @@ def data_model_schema() -> dict[str, Any]:
         return DataModel.model_json_schema()
 
     @mcp.tool()
-    def validate_node(node: Node) -> bool:
+    def validate_node(node: Node) -> list[mcp_types.TextContent]:
         "Validate a single node. Returns True if the node is valid, otherwise raises a ValueError."
         logger.info("Validating a single node.")
         try:
             Node.model_validate(node, strict=True)
+            logger.info(f"Node validated successfully")
+            return True
         except ValidationError as e:
             logger.error(f"Validation error: {e}")
             raise ValueError(f"Validation error: {e}")
-        logger.info("Node validated successfully.")
-        return True
+        
 
     @mcp.tool()
     def validate_relationship(relationship: Relationship) -> bool:
         "Validate a single relationship. Returns True if the relationship is valid, otherwise raises a ValueError."
         logger.info("Validating a single relationship.")
         try:
             Relationship.model_validate(relationship, strict=True)
+            logger.info(f"Relationship validated successfully")
+            return True
         except ValidationError as e:
             logger.error(f"Validation error: {e}")
             raise ValueError(f"Validation error: {e}")
-        logger.info("Relationship validated successfully.")
-        return True
+
 
     @mcp.tool()
     def validate_data_model(data_model: DataModel) -> bool:
         "Validate the entire data model. Returns True if the data model is valid, otherwise raises a ValueError."
         logger.info("Validating the entire data model.")
         try:
             DataModel.model_validate(data_model, strict=True)
+            logger.info(f"Data model validated successfully")
+            return True
         except ValidationError as e:
             logger.error(f"Validation error: {e}")
             raise ValueError(f"Validation error: {e}")
-        logger.info("Data model validated successfully.")
-        return True
+        
 
     @mcp.tool()
     def visualize_data_model(data_model: DataModel) -> None:
-        "Open an interactive graph visualization in the default web browser."
+        "Open an interactive graph visualization in the default web browser. Warning: May not be useable in Docker environments."
         logger.info("Validating the data model.")
         try:
             dm_validated = DataModel.model_validate(data_model, strict=True)