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MCP Context Manager MCP Server

mcp-context-manager

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "bswa006-mcp-context-manager": {
      "command": "npx",
      "args": [
        "mcp-context-manager"
      ]
    }
  }
}

The MCP Context Manager server empowers AI-assisted development by providing a structured, secure, and memory-backed context management layer. It helps AI agents write high-quality code with built-in pattern validation, security checks, and automated testing, while keeping token usage efficient and behavior predictable.

How to use

To use the MCP Context Manager, run the MCP client on your development machine or within your CI environment, then connect your AI agent to the server configuration you choose. You can run the server locally via the provided CLI or attach it through your IDE/client configuration. Once connected, the agent will load project constraints, detect existing patterns, enforce security and error handling rules, and generate code that adheres to your project’s conventions.

Key usage patterns include: scanning your codebase for patterns, verifying imports and APIs before suggesting changes, generating tests to achieve high coverage, and continuously tracking performance and security metrics. The workflow emphasizes validation at every step so generated code matches established conventions and security requirements.

How to install

Prerequisites: ensure you have Node.js and npm installed on your machine.

Option A: Install the MCP Context Manager CLI (recommended)

# Install globally
npm install -g mcp-context-manager

# Or use directly with npx
npx mcp-context-manager

Option B: Build locally from source (if you prefer to clone the repository)

# Clone the repository
git clone https://github.com/bswa006/mcp-context-manager
cd mcp-context-manager

# Install dependencies
npm install

# Build the server
npm run build

After installation or build, you can proceed to configure your client to load the MCP server and connect to it as described in the configuration steps below.

Configuration and usage notes

Configure your MCP server connection in your AI client settings so the agent can load the MCP context, memory, and validation rules. The server exposes a simple, explicit runtime path that your client can invoke from the CLI or via an IDE integration.

Additional considerations

- Ensure your project includes the patterns and constraints you want the AI to follow. Regularly update the pattern library to keep AI guidance aligned with current conventions.

- Maintain a clear memory of lessons learned by your agents so they improve over time. Use the built-in performance and security metrics to track progress.

Security and quality emphasis

Security rules are enforced during code generation, and AI agents verify APIs and method availability before usage. Tests are generated to achieve high coverage, and code is validated against established patterns before being shared.

Information on available integrations

The MCP Context Manager supports IDE integrations and automated tooling to streamline setup and ongoing maintenance, including token optimization and persistent context features to reduce repetitive work.

Troubleshooting tips

If you encounter issues, verify that the MCP client is correctly connected to the server and that your project patterns are loaded. Check for updated patterns and re-run validations to ensure alignment with your conventions.

Notes on usage in Claude Desktop and Cursor

You can configure MCp connections in Claude Desktop and Cursor to point at the MCP server runner. Use the provided sample configurations to ensure the agent loads the server and its context resources at startup.

Available tools

check_before_suggesting

Prevents hallucinations by verifying imports, methods, and patterns exist before AI suggests code.

validate_generated_code

Validates AI output against project patterns and conventions.

get_pattern_for_task

Provides the exact pattern to follow for a given task type.

check_security_compliance

Conducts security validation to catch vulnerabilities before suggestions.

detect_existing_patterns

Analyzes the codebase to match coding style and patterns.

initialize_agent_workspace

Creates a complete AI agent workspace with templates and context.

generate_tests_for_coverage

Generates tests to achieve 80%+ code coverage with edge cases.

track_agent_performance

Tracks metrics such as tokens used, validation scores, and coverage.

analyze_codebase_deeply

Performs deep, comprehensive analysis of the codebase for patterns and architecture.

create_conversation_starters

Generates prompts to quickly convey project context to the AI.

create_token_optimizer

Produces tiered context files to optimize token usage and ROI.

create_ide_configs

Creates IDE-specific configurations for Cursor, VS Code, and IntelliJ.

setup_persistence_automation

Sets up automated context updates with monitoring and validation.

create_maintenance_workflows

Generates team workflows for maintaining AI context quality.

complete_setup_workflow

Executes a complete setup: analyze, configure, and automate.