RFC: Testing And Validation Framework For Model Context Protocol Knowledge Graph Memory Server

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Abstract

This document proposes a comprehensive testing and validation framework for the Model Context Protocol (MCP) Knowledge Graph Memory Server. The framework aims to ensure the server's reliability, functionality, and performance through structured testing approaches, focusing on both automated and manual validation methods.

Background

The MCP Knowledge Graph Memory Server provides critical capabilities for AI agents to store, retrieve, and manipulate knowledge through a graph-based representation system. As observed through its tool listing, it supports operations such as creating entities and relations, adding observations, and searching nodes. These capabilities require thorough validation to ensure they function correctly and efficiently in various contexts and under different loads.

Motivation

Robust testing is essential for:

  • Ensuring data integrity within the knowledge graph
  • Validating that query results are accurate and consistent
  • Verifying that the server handles edge cases appropriately
  • Confirming performance meets requirements under expected loads
  • Detecting regression issues in new server versions

Testing Framework Components

Unit Testing

Unit tests should validate the core functionality of each tool exposed by the server:

# Test structure for create_entities
echo '{"jsonrpc": "2.0", "method": "tools/call", "params": {"name": "create_entities", "input": {"entities": [{"name": "Test Entity", "entityType": "TestType", "observations": ["Test observation"]}]}}, "id": 1}' | npx -y @modelcontextprotocol/server-memory

Each tool should have dedicated test cases verifying:

  • Valid input handling
  • Invalid input rejection with appropriate error messages
  • Edge cases (empty arrays, very large inputs, special characters)
  • Idempotent operations where applicable

Integration Testing

Integration tests should validate the interactions between different tools:

  1. Create entities, then retrieve them
  2. Create relations between entities, then query based on those relations
  3. Add observations to entities, then search based on observation content
  4. Delete entities and verify cascading effects on relations

Performance Testing

Performance tests should measure:

  • Response time for various operations under different graph sizes
  • Memory usage patterns during extended operations
  • Throughput capabilities (operations per second)
  • Recovery time after errors or unexpected shutdowns

Consistency Testing

Tests to verify data consistency:

  • Verify that created entities persist across server restarts
  • Confirm that search queries return consistent results for identical inputs
  • Validate that related entities maintain bidirectional relationships where applicable

Test Automation

Test Harness

A dedicated test harness should be created with the following capabilities:

import json
import subprocess
import time

class MCPTestHarness:
    def __init__(self, server_command="npx -y @modelcontextprotocol/server-memory"):
        self.server_command = server_command
        self.process = None
        
    def start_server(self):
        # Start server as a subprocess
        pass
        
    def stop_server(self):
        # Stop server gracefully
        pass
        
    def send_command(self, method, params, id=1):
        # Construct and send JSON-RPC command
        command = {
            "jsonrpc": "2.0",
            "method": method,
            "params": params,
            "id": id
        }
        # Send to server and return result
        pass
        
    def run_test_suite(self, tests):
        # Run a series of tests and collect results
        pass

CI/CD Integration

Tests should be integrated into CI/CD pipelines to:

  • Run all tests on each code commit
  • Generate test coverage reports
  • Compare performance metrics across versions
  • Automate deployment after successful test completion

Validation Scenarios

Basic Functionality Validation

Test Case: Create and Retrieve Entity
1. Create a new entity with name "TestObject", type "PhysicalObject", observation ["Color: Blue"]
2. Read the graph to verify the entity exists with correct properties

Complex Scenario Validation

Test Case: Knowledge Graph Construction and Query
1. Create multiple entities of different types (Person, Location, Event)
2. Create relations between these entities
3. Add observations to entities based on their relationships
4. Search for entities based on relationship patterns
5. Delete a central entity and verify cascading effects

Error Case Validation

Test Case: Invalid Input Handling
1. Attempt to create an entity with a name that already exists
2. Try to create a relation between non-existent entities
3. Send malformed JSON requests
4. Test with excessive payload sizes

Validation Metrics

The following metrics should be tracked during testing:

  1. Functional Coverage: Percentage of server features tested
  2. Code Coverage: Percentage of codebase exercised by tests
  3. Error Rate: Frequency of errors during standard operations
  4. Response Time: Time taken to complete various operations
  5. Resource Usage: CPU, memory, and disk I/O during operations

Manual Testing Guidelines

While automated testing provides coverage, manual testing is essential for validating:

  1. Usability: How intuitive are the tool interfaces?
  2. Documentation Accuracy: Do operations match their documentation?
  3. Edge Cases: Scenarios difficult to automate (network issues, resource exhaustion)
  4. Integration with LLMs: How effectively can AI models utilize the server's capabilities?

Implementation Plan

Phase 1: Test Framework Development

  • Develop test harness
  • Create basic unit tests for each tool
  • Establish CI/CD integration

Phase 2: Comprehensive Test Suite

  • Develop integration tests
  • Implement performance test suite
  • Create consistency validation tests

Phase 3: Documentation and Reporting

  • Document test procedures
  • Set up automated test reporting
  • Create dashboards for test results visualization

Example Test Implementation

# test_knowledge_graph.py
import unittest
from mcp_test_harness import MCPTestHarness

class TestKnowledgeGraph(unittest.TestCase):
    def setUp(self):
        self.harness = MCPTestHarness()
        self.harness.start_server()
        
    def tearDown(self):
        self.harness.stop_server()
        
    def test_entity_creation(self):
        # Test creating a single entity
        result = self.harness_command(
            "tools/call",
            {
                "name": "create_entities",
                "input": {
                    "entities": [
                        {
                            "name": "TestEntity",
                            "entityType": "TestType",
                            "observations": ["Test observation"]
                        }
                    ]
                }
            }
        )
        self.assertEqual(result.get("error", None), None)
        
        # Verify entity exists
        graph = self.harness.send_command(
            "tools/call",
            {
                "name": "read_graph",
                "input": {}
            }
        )
        entities = graph["result"]["content"][0]["entities"]
        self.assertTrue(any(e["name"] == "TestEntity" for e in entities))

Makefile Implementation for Common Test Operations

# Makefile for MCP Knowledge Graph Server Testing

# Server configuration
SERVER_CMD = npx -y @modelcontextprotocol/server-memory

# Test basic server operation
test-server-start:
	@echo "Testing server startup..."
	@$(SERVER_CMD) --version

# List available tools
list-tools:
	@echo '{"jsonrpc": "2.0", "method": "tools/list", "id": 1}' | $(SERVER_CMD) | jq

# Run unit tests
test-unit:
	@python -m unittest discover -s tests/unit

# Run integration tests
test-integration:
	@python -m unittest discover -s tests/integration

# Run performance tests
test-performance:
	@python tests/performance/run_benchmarks.py

# Run all tests
test-all: test-unit test-integration test-performance

# Clean test artifacts
clean:
	@rm -rf test-results/* coverage-reports/*

Conclusion

This testing and validation framework provides a comprehensive approach to ensuring the quality, reliability, and performance of the MCP Knowledge Graph Memory Server. By implementing both automated and manual testing procedures, we can maintain confidence in the server's capabilities while supporting ongoing development and enhancement.

Appendix A: Test Case Template

Test ID: [Unique identifier]
Test Name: [Brief descriptive name]
Objective: [What the test aims to validate]
Prerequisites: [Any required setup]
Test Steps:
  1. [Step description]
  2. ...
Expected Results: [What should happen]
Actual Results: [Record during testing]
Pass/Fail Criteria: [How to determine success]
Notes: [Any additional information]

Appendix B: Sample Test Suite Structure

tests/
├── unit/
│   ├── test_create_entities.py
│   ├── test_create_relations.py
│   ├── test_add_observations.py
│   └── ...
├── integration/
│   ├── test_entity_relation_workflows.py
│   ├── test_search_capabilities.py
│   └── ...
├── performance/
│   ├── test_large_graph_operations.py
│   ├── test_concurrent_requests.py
│   └── ...
└── utils/
    ├── test_harness.py
    ├── data_generators.py
    └── result_analyzers.py
```<br/>
**Q&A: Testing and Validation Framework for Model Context Protocol Knowledge Graph Memory Server**
=====================================================================================

**Q: What is the purpose of the testing and validation framework for the Model Context Protocol Knowledge Graph Memory Server?**
---------------------------

A: The purpose of the testing and validation framework is to ensure the quality, reliability, and performance of the MCP Knowledge Graph Memory Server. It aims to provide a comprehensive approach to testing and validation, covering both automated and manual testing procedures.

**Q: What are the key components of the testing framework?**
---------------------------------------------------

A: The key components of the testing framework include:

* Unit testing: Verifies the core functionality of each tool exposed by the server
* Integration testing: Validates the interactions between different tools
* Performance testing: Measures the response time, memory usage, and throughput capabilities of the server
* Consistency testing: Verifies data consistency and bidirectional relationships between entities

**Q: What is the role of the test harness in the testing framework?**
----------------------------------------------------------------

A: The test harness is a dedicated tool that provides a set of capabilities for running tests, including starting and stopping the server, sending JSON-RPC commands, and running test suites.

**Q: How does the testing framework ensure data integrity within the knowledge graph?**
--------------------------------------------------------------------------------

A: The testing framework includes tests to verify that created entities persist across server restarts, and that search queries return consistent results for identical inputs.

**Q: What is the importance of manual testing in the testing framework?**
-------------------------------------------------------------------

A: Manual testing is essential for validating usability, documentation accuracy, edge cases, and integration with LLMs. It provides a human perspective on the server's capabilities and helps identify issues that may not be caught by automated testing.

**Q: How does the testing framework support ongoing development and enhancement of the MCP Knowledge Graph Memory Server?**
-----------------------------------------------------------------------------------------

A: The testing framework provides a comprehensive approach to testing and validation, which helps maintain confidence in the server's capabilities and supports ongoing development and enhancement.

**Q: What are the benefits of integrating the testing framework into CI/CD pipelines?**
--------------------------------------------------------------------------------

A: Integrating the testing framework into CI/CD pipelines allows for:

* Running all tests on each code commit
* Generating test coverage reports
* Comparing performance metrics across versions
* Automating deployment after successful test completion

**Q: How can the testing framework be customized to meet the specific needs of the MCP Knowledge Graph Memory Server?**
-----------------------------------------------------------------------------------------

A: The testing framework can be customized by modifying the test harness, adding or removing test cases, and integrating with other testing tools and frameworks.

**Q: What is the expected outcome of the testing framework?**
---------------------------------------------------

A: The expected outcome of the testing framework is to ensure the quality, reliability, and performance of the MCP Knowledge Graph Memory Server, and to provide a comprehensive approach to testing and validation.

**Q: How can the testing framework be maintained and updated over time?**
-------------------------------------------------------------------

A: The testing framework can be maintained and updated by:

* Regularly reviewing and updating test cases
* Integrating with new testing tools and frameworks
* Modifying the test harness to accommodate changes in the server's capabilities
* Providing training and support for testing and validation procedures

**Q: What is the role of the testing framework in ensuring the security of the MCP Knowledge Graph Memory Server?**
-----------------------------------------------------------------------------------------

A: The testing framework includes tests to verify the server's handling of edge cases, such as network issues and resource exhaustion, which can help ensure the security of the server.

**Q: How can the testing framework be used to support the development of new features and capabilities for the MCP Knowledge Graph Memory Server?**
-----------------------------------------------------------------------------------------

A: The testing framework can be used to support the development of new features and capabilities by:

* Providing a comprehensive approach to testing and validation
* Identifying potential issues and areas for improvement
* Ensuring that new features and capabilities meet the required standards for quality, reliability, and performance.