MCP Context Provider

The stable, glowing orb at the center represents the persistent context that survives across chat sessions. The flowing data streams show how ongoing conversations connect to and draw from this stable core of information, preventing context loss.

A static MCP (Model Context Protocol) server that provides AI models with persistent tool context, preventing context loss between chat sessions. This server automatically loads and injects tool-specific rules, syntax preferences, and best practices at Claude Desktop startup.

Overview

The Context Provider acts as a persistent neural core for your AI interactions, eliminating the need to re-establish context in each new chat session by:

🔄 Persistent Context: Like the stable orb in the visualization, rules and preferences survive across Claude Desktop restarts
⚡ Automatic Injection: Context flows seamlessly into every conversation, just as the data streams connect to the central core
🎯 Tool-Specific: Each tool gets its own context rules and syntax preferences, creating specialized knowledge pathways
🔧 Auto-Corrections: Automatic syntax transformations (e.g., Markdown → DokuWiki) ensure consistency across all interactions
📈 Scalable: Easy to add new tools and context rules, expanding the knowledge network
🏢 Enterprise-Ready: Version-controlled context management provides organizational stability

The Neural Network Metaphor

Just like the image depicts, your MCP Context Provider functions as:

Central Orb: The stable, persistent context core that maintains consistency
Neural Pathways: Tool-specific context rules that create specialized knowledge channels
Data Streams: Individual chat sessions that flow through and benefit from the persistent context
Network Stability: Prevents the ephemeral nature of conversations from losing important contextual information

Quick Start

Option 1: Automated Installation (Recommended)

The easiest way to install MCP Context Provider is using the provided installation scripts:

Unix/Linux/macOS:

# Clone the repository (contains latest source files)
git clone https://github.com/doobidoo/MCP-Context-Provider.git
cd MCP-Context-Provider

# Run the automated installer (builds fresh package)
./scripts/install.sh

Windows:

# Clone the repository first
git clone https://github.com/doobidoo/MCP-Context-Provider.git
cd MCP-Context-Provider

# Run the Windows installer
.\scripts\install.bat

The installation script automatically:

Builds the latest DXT package from source
Creates a Python virtual environment
Installs all required dependencies
Configures Claude Desktop settings

Option 2: Manual Installation from DXT

# Install DXT CLI (if not already installed)
npm install -g @anthropic-ai/dxt

# Download the DXT package
wget https://github.com/doobidoo/MCP-Context-Provider/raw/main/mcp-context-provider-1.2.1.dxt

# Unpack the extension to your desired location
dxt unpack mcp-context-provider-1.2.1.dxt ~/mcp-context-provider

# Navigate to the installation directory
cd ~/mcp-context-provider

# Create and activate a Python virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install mcp>=1.9.4

Option 3: Installation from Source

# Clone the repository
git clone https://github.com/doobidoo/MCP-Context-Provider.git
cd MCP-Context-Provider

# Create and activate a Python virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Configuration

Update your Claude Desktop configuration file:

Configuration File Location:

Linux: ~/.config/claude/claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

For Virtual Environment Installation (Recommended):

{
  "mcpServers": {
    "context-provider": {
      "command": "/path/to/mcp-context-provider/venv/bin/python",
      "args": ["/path/to/mcp-context-provider/context_provider_server.py"],
      "env": {
        "CONTEXT_CONFIG_DIR": "/path/to/mcp-context-provider/contexts",
        "AUTO_LOAD_CONTEXTS": "true"
      }
    }
  }
}

For System Python Installation:

{
  "mcpServers": {
    "context-provider": {
      "command": "python",
      "args": ["context_provider_server.py"],
      "cwd": "/path/to/MCP-Context-Provider",
      "env": {
        "CONTEXT_CONFIG_DIR": "./contexts",
        "AUTO_LOAD_CONTEXTS": "true"
      }
    }
  }
}

Important: Replace /path/to/mcp-context-provider with the actual installation path.

3. Verify Installation

Run the verification script to ensure everything is configured correctly:

python scripts/verify_install.py

4. Restart Claude Desktop

After updating the configuration, restart Claude Desktop to load the MCP server.

How It Works

Architecture

Context Provider Server: Python MCP server that loads JSON context files
Context Files: Tool-specific rules stored in /contexts directory
Claude Desktop Integration: MCP server registered in configuration
Automatic Loading: Context is injected at startup and persists across chats

Context Flow

Startup → Load Context Files → Register MCP Tools → Context Available in All Chats

Available Tools

Once loaded, the following tools are available in all chat sessions:

Core Context Tools:

get_tool_context: Get context rules for specific tool
get_syntax_rules: Get syntax conversion rules
list_available_contexts: List all loaded context categories
apply_auto_corrections: Apply automatic syntax corrections

Phase 1 - Session Management:

execute_session_initialization: Initialize session with memory service integration
get_session_status: Retrieve detailed session initialization status

Phase 2 - Dynamic Context Management:

create_context_file: Create new context files dynamically with validation
update_context_rules: Update existing context rules with backup and validation
add_context_pattern: Add patterns to auto-trigger sections for memory integration

Phase 3 - Intelligent Learning (v1.6.0+):

analyze_context_effectiveness: Analyze context effectiveness with memory-driven insights
suggest_context_optimizations: Generate global optimization suggestions based on usage patterns
get_proactive_suggestions: Provide proactive context suggestions for workflow improvement
auto_optimize_context: Automatically optimize contexts based on learning engine recommendations

Screenshot showing the MCP Context Provider in action within Claude Desktop. The tool automatically detects and lists all available context categories (dokuwiki, terraform, azure, git, general_preferences) and provides interactive access to tool-specific rules and guidelines.

Context Files

The server loads context files from the /contexts directory:

dokuwiki_context.json: DokuWiki syntax rules and preferences
terraform_context.json: Terraform naming conventions and best practices
azure_context.json: Azure resource naming and compliance rules
git_context.json: Git commit conventions and workflow patterns
general_preferences.json: Cross-tool preferences and standards

Context File Structure

Each context file follows this pattern:

{
  "tool_category": "toolname",
  "description": "Tool-specific context rules",
  "auto_convert": true,
  "syntax_rules": {
    "format_rules": "conversion patterns"
  },
  "preferences": {
    "user_preferences": "settings"
  },
  "auto_corrections": {
    "regex_patterns": "automatic fixes"
  },
  "metadata": {
    "version": "1.0.0",
    "applies_to_tools": ["tool:*"]
  }
}

Examples

DokuWiki Syntax Conversion

Input (Markdown):

# My Header
This is `inline code` and here's a [link](http://example.com).

Auto-converted to DokuWiki:

====== My Header ======
This is ''inline code'' and here's a [[http://example.com|link]].

Azure Resource Naming

Input: storage_account_logs_prod Auto-corrected to: stlogsprod (following Azure naming conventions)

Git Commit Messages

Input: Fixed the login bug Auto-corrected to: fix: resolve login authentication issue

Adding New Context

To add support for a new tool:

Create a new JSON file: contexts/{toolname}_context.json
Follow the standard context structure
Restart Claude Desktop to load the new context

The server automatically detects and loads any *_context.json files in the contexts directory.

Benefits

For Developers

No need to re-establish context in new chats
Automatic syntax corrections save time
Consistent formatting across all work
Best practices automatically applied

For Teams

Shared context rules across team members
Version-controlled standards
Consistent code and documentation formatting
Enterprise compliance automatically enforced

For Organizations

Centralized context management
Scalable across multiple tools
Audit trail of context changes
Easy deployment and updates

🧠 Phase 3: Intelligent Learning System (v1.6.0+)

Revolutionary Learning Capabilities

Version 1.6.0 introduces the Synergistic Integration with Intelligent Learning system, transforming the MCP Context Provider from a static configuration tool into an intelligent, self-improving context evolution platform.

🎯 Key Learning Features

Intelligent Context Evolution

Automatic Effectiveness Analysis: Contexts self-analyze based on usage patterns and memory data
Smart Optimization Suggestions: AI-driven recommendations for context improvements
Auto-Optimization: Contexts automatically improve through pattern learning and preference tuning
Proactive Intelligence: Suggests missing tool contexts and workflow enhancements

Real Memory Service Integration

Persistent Learning: Full integration with mcp-memory-service for persistent learning data
Usage Pattern Tracking: Comprehensive tracking of context modifications and effectiveness
Memory-Driven Insights: Historical data analysis for continuous improvement
Team Knowledge Propagation: Shared learning insights across team members

Advanced MCP Tools

4 new intelligent tools for context management:

analyze_context_effectiveness: Memory-driven effectiveness analysis
suggest_context_optimizations: Global optimization recommendations
get_proactive_suggestions: Workflow improvement suggestions
auto_optimize_context: Automatic context optimization based on learning

🔄 Learning Workflow

Context Usage → Memory Storage → Pattern Analysis → Optimization Suggestions → Auto-Improvement
     ↓              ↓                 ↓                    ↓                     ↓
Session Data → Learning Engine → Effectiveness Score → Proactive Recommendations → Enhanced Contexts

📊 Learning Metrics

The system tracks and analyzes:

Context Effectiveness Scores (0.0-1.0 scale)
Usage Pattern Recognition (frequency, modifications, interactions)
Session Performance Optimization (sub-second initialization targets)
Memory-Driven Trend Analysis (historical usage and improvement data)

🚀 Phase 3 Setup

Prerequisites: Requires mcp-memory-service integration

Configure Memory Service (.mcp.json):

{
  "mcpServers": {
    "memory": {
      "command": "/path/to/uv",
      "args": ["--directory", "/path/to/mcp-memory-service", "run", "memory"],
      "env": {
        "MCP_MEMORY_STORAGE_BACKEND": "sqlite_vec",
        "MCP_MEMORY_SQLITE_PATH": "/path/to/memory.db"
      }
    },
    "context-provider": {
      "command": "python",
      "args": ["context_provider_server.py"],
      "env": {
        "CONTEXT_CONFIG_DIR": "./contexts",
        "AUTO_LOAD_CONTEXTS": "true"
      }
    }
  }
}

Test Learning Features:

# Run comprehensive Phase 3 tests
python tests/test_phase3_learning.py

# Check learning engine health
python -c "
from context_provider_server import ContextProvider
import asyncio
async def test():
    provider = ContextProvider()
    stats = await provider.memory_service.get_memory_stats()
    print(f'Learning system status: {stats}')
asyncio.run(test())
"

📚 Phase 3 Documentation

Learning System Guide: Comprehensive guide to intelligent features
API Reference: Complete API documentation for learning components
Examples & Use Cases: Practical examples and enterprise use cases

🎉 Implementation Roadmap Complete

✅ Phase 1: Session initialization with memory service integration ✅ Phase 2: Dynamic context file creation and management ✅ Phase 3: Synergistic integration with intelligent learning

The MCP Context Provider now offers enterprise-ready intelligent context evolution with self-improving contexts that learn from usage patterns and automatically optimize through real memory service integration.

Advanced Usage

Custom Context Rules

Create your own context files by following the established pattern. The server supports:

Regex-based auto-corrections
Tool-specific preferences
Conditional formatting rules
Multi-tool context inheritance

Environment-Specific Context

Use environment variables to load different context sets:

{
  "env": {
    "CONTEXT_CONFIG_DIR": "./contexts/production",
    "ENVIRONMENT": "prod"
  }
}

Troubleshooting

Common Issues

Context not loading: Check file path in Claude Desktop config
Server not starting: Verify Python dependencies installed
Rules not applying: Check JSON syntax in context files

See TROUBLESHOOTING.md for detailed solutions.

Documentation

Context Guide: Complete context file reference
Developer Guide: Creating custom contexts
Examples: Real-world usage examples
Troubleshooting: Common issues and solutions

📚 Wiki & Use Cases

Explore advanced integrations and real-world use cases in our Community Wiki:

Wiki Homepage: Comprehensive guide to what the Context Provider is good for
AppleScript with Memory Integration: Advanced workflow showcasing intelligent script management with persistent memory
Integration Examples: Community-driven examples of Context Provider workflows
Best Practices: Tips and patterns for maximizing Context Provider effectiveness

The wiki demonstrates how the Context Provider transforms from simple rule storage into intelligent, self-improving workflow automation.

DXT Package Distribution

The MCP Context Provider is available as a Desktop Extension (DXT) package for easy distribution and installation:

Package: mcp-context-provider-1.0.0.dxt (18.6 MB)
Contents: Complete server with all dependencies bundled
Platform: Windows, macOS, Linux with Python 3.8+
Dependencies: Self-contained (no external pip requirements)

Building DXT Package

To build your own DXT package from source:

# Install DXT CLI
npm install -g @anthropic-ai/dxt

# Build the package
cd dxt
dxt pack

# The package will be created as mcp-context-provider-1.0.0.dxt

Distribution Notes

The DXT package includes all Python dependencies (MCP SDK, Pydantic, etc.)
Total unpacked size: ~45 MB including all dependencies
Optimized for offline installation and deployment
Compatible with corporate environments and air-gapped systems

Contributing

Fork the repository
Create a feature branch: git checkout -b feature/new-context
Add your context file to /contexts
Test with your Claude Desktop setup
Submit a pull request

License

MIT License - see LICENSE file for details.

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Provides persistent tool context that survives across Claude Desktop chat sessions, automatically injecting tool-specific rules, syntax preferences, and best practices. Eliminates the need to re-establish context in each new conversation.

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