Model Context Protocol (MCP)

Introduction

The Model Context Protocol (MCP) is an open standard designed to create standardized connections between AI models and external data sources or tools. Similar to how USB-C provides a standardized connection for devices and peripherals, MCP offers a standardized way to connect AI models to various data sources and tools.

MCP addresses a fundamental limitation of AI models: their isolation from data sources. Even advanced models are constrained when trapped behind information silos and legacy systems. MCP solves this by providing a universal protocol for connecting AI systems with data sources, replacing fragmented custom integrations with a standardized approach.

Key Architecture Components

1. MCP Hosts

MCP Hosts are programs that want to access data and functionality through the MCP protocol. These are typically AI-powered applications that need to connect to external data sources or tools.

Definition and Role:

• MCP Hosts are applications that leverage AI capabilities and want to connect to external data or functionality
• They initiate the connection to MCP Servers through MCP Clients
• They process the results returned from MCP Servers to enhance AI responses

Examples of MCP Hosts:

• AI-powered IDEs (Integrated Development Environments)
• Chat interfaces with AI capabilities
• Document processing applications
• Knowledge management systems
• Custom AI workflows in business applications

Local Implementation Approaches:

• Running self-hosted AI chat interfaces that connect to local MCP servers
• Embedding MCP host capabilities in custom applications
• Developing specialized domain-specific applications that need to connect to local data sources
• Creating desktop applications that leverage local LLMs while maintaining privacy

2. MCP Clients

MCP Clients maintain the actual protocol-level connections between MCP Hosts and MCP Servers. They handle the communication layer of the MCP architecture.

Definition and Role:

• MCP Clients implement the client-side of the MCP protocol
• They maintain 1:1 connections with MCP Servers
• They translate requests from MCP Hosts into the proper protocol format
• They handle authentication, security, and session management

Examples of MCP Clients:

• Libraries or SDKs that implement the MCP client protocol
• Client components within larger applications
• Middleware that connects applications to MCP Servers
• Specialized connectors for specific types of AI systems

Local Implementation Approaches:

• Using MCP client libraries in Python, JavaScript, or other languages
• Implementing the MCP client specification directly in your application
• Using containerized MCP clients that can be orchestrated with Docker/Podman
• Building proxy clients that can route requests to multiple servers

3. MCP Servers

MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They connect to data sources and provide structured access to them.

Definition and Role:

• MCP Servers implement the server-side of the MCP protocol
• They expose capabilities, data sources, and tools through standardized interfaces
• They process requests from MCP Clients and return formatted responses
• They handle the actual connections to data sources or functional systems

Examples of MCP Servers:

• Document repositories (connecting to file systems, document databases)
• Code repositories (connecting to GitHub, GitLab, local git repositories)
• Database connectors (connecting to PostgreSQL, MongoDB, etc.)
• Knowledge graph interfaces
• Tool executors (running specific tools and returning results)

Local Implementation Approaches:

• Creating containerized MCP servers for specific data sources
• Building custom MCP servers for proprietary data formats
• Implementing MCP servers that connect to local knowledge graphs
• Developing MCP servers that provide structured access to local document repositories

Implementation for Local Models with Docker/Podman

For running local models and integrating local data assets without relying on third-party services, here’s a comprehensive approach:

Container-Based Architecture

A Docker/Podman-based implementation could include the following containers:

1. Local LLM Container
   • Runs the actual language model (e.g., Llama, Falcon, Mistral)
   • Exposes an API for text generation
   • Configured for optimal performance on your hardware
2. MCP Host Container
   • Implements the application logic that interacts with the user
   • Contains the UI layer (web interface, API, or CLI)
   • Connects to the MCP Client to access external data
3. MCP Client Container
   • Implements the MCP protocol
   • Manages connections to various MCP Servers
   • Handles authentication and session management
4. MCP Server Containers (multiple)
   • Document Server: Connects to local document storage
   • Database Server: Connects to PostgreSQL or other databases
   • Knowledge Graph Server: Provides access to local knowledge graphs
   • Tool Server: Executes specific tools and returns results

Data Integration Strategies

1. Volume Mounting
   • Mount local data directories into MCP Server containers
   • Ensures data privacy while providing access to the AI system
2. Local Network Communication
   • Set up a private Docker network for communication between containers
   • Implement secure communication protocols between components
3. Persistent Storage
   • Use Docker volumes for persistent storage of data
   • Maintain state between container restarts

Example Configuration: docker-compose.yml

name: mcp

services:
  local-llm:
    image: your-local-llm-image
    volumes:
      - ./model-weights:/app/models
    ports:
      - "8000:8000"  # Model API port
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

  mcp-host:
    image: your-mcp-host-image
    ports:
      - "3000:3000"  # Web UI port
    depends_on:
      - local-llm
      - mcp-client

  mcp-client:
    image: your-mcp-client-image
    depends_on:
      - local-llm

  document-server:
    image: your-document-server-image
    volumes:
      - ./documents:/app/data
    depends_on:
      - mcp-client

  database-server:
    image: your-database-server-image
    environment:
      - POSTGRES_CONNECTION_STRING=postgresql://user:password@postgres:5432/db
    depends_on:
      - postgres
      - mcp-client

  knowledge-graph-server:
    image: your-knowledge-graph-server-image
    volumes:
      - ./knowledge-graph:/app/data
    depends_on:
      - mcp-client

  postgres:
    image: postgres:14
    environment:
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=password
      - POSTGRES_DB=db
    volumes:
      - postgres-data:/var/lib/postgresql/data

volumes:
  postgres-data:

Building Custom MCP Servers for Local Data

Document Repository Server

A custom MCP server for local document repositories could:

• Index local files (PDFs, Markdown, Word documents)
• Provide search capabilities across these documents
• Return contextually relevant document snippets based on queries
• Support document updates and versioning

Example implementation approach:

1. Use a local vector database for semantic search (e.g., Chroma, FAISS)
2. Implement document preprocessing pipeline for various formats
3. Expose MCP-compliant endpoints for searching and retrieving documents

Knowledge Graph Server

A knowledge graph MCP server could:

• Connect to a local graph database (e.g., Neo4j)
• Translate natural language queries into graph queries
• Return structured knowledge with relationship information
• Support updating the knowledge graph based on new information

Local Database Connector

For PostgreSQL integration:

• Implement SQL query generation from natural language
• Provide database schema information to the LLM
• Support data visualization capabilities
• Handle authentication and access control

Security Considerations for Local Deployment with CMMC 2.0 and SOC2 Type II Compliance

Implementing Model Context Protocol for local models with sensitive data requires robust security measures, particularly when compliance with standards like CMMC 2.0 and SOC2 Type II is necessary. Here are comprehensive security considerations with concrete examples and best practices:

1. Authentication and Authorization

CMMC 2.0 Level 2 Compliant Authentication:

1. Multi-Factor Authentication Implementation
   • Example: Configure MFA for all MCP administrative interfaces using hardware security keys as the primary second factor, with time-based one-time passwords (TOTP) as backup.
   • Best Practice: Implement FIDO2-compliant hardware tokens that support passwordless authentication while meeting NIST SP 800-171 requirements.

     # Example Docker configuration with MFA security volume
     services:
     mcp-host:
       image: your-mcp-host-image
       volumes:
         - ./security/mfa:/app/security/mfa
       environment:
         - MFA_REQUIRED=true
         - MFA_TIMEOUT_MINUTES=30
         - MFA_HARDWARE_KEY_REQUIRED=true
     

2. Role-Based Access Control with Least Privilege
   • Example: Create granular RBAC profiles for different user types (administrators, analysts, auditors) with permissions strictly limited to job functions.
   • Best Practice: Implement dynamic access controls that adjust permissions based on context (time of day, location, device posture).

# Example RBAC configuration for MCP server
authorization:
    default: deny
    roles:
    - name: mcp-admin
        resources: ["configuration", "users", "connections"]
        verbs: ["get", "list", "create", "update", "delete"]
    - name: mcp-auditor
        resources: ["logs", "metrics", "connections"]
        verbs: ["get", "list"]
    - name: mcp-user
        resources: ["connections"]
        verbs: ["get", "list", "create"]

1. Mutual TLS (mTLS) Between All Components
   • Example: Configure mutual TLS between all MCP components with certificate rotation every 30 days and certificate-based workload identity.
   • Best Practice: Use a dedicated PKI infrastructure with hardware security modules (HSMs) for key protection.

# Example mTLS configuration
security:
    mtls:
    enabled: true
    cert_rotation_days: 30
    min_tls_version: "TLS1.3"
    cipher_suites:
        - TLS_AES_256_GCM_SHA384
        - TLS_CHACHA20_POLY1305_SHA256
    verify_client: true
    ca_cert: "/path/to/ca.crt"

2. Data Privacy

SOC2 Type II Compliant Data Handling:

1. Data Classification and Handling
   • Example: Implement automatic data classification that tags data as public, internal, confidential, or restricted, with corresponding protection mechanisms.
   • Best Practice: Use content-based classification that analyzes data patterns to identify sensitive information (like PII, PHI) automatically.

# Example data classification configuration
data_classification:
    enabled: true
    scan_schedule: "0 */4 * * *"  # Every 4 hours
    patterns:
    - name: "PII"
        regex: "[0-9]{3}-[0-9]{2}-[0-9]{4}"  # SSN pattern
        classification: "restricted"
    - name: "Credit Card"
        regex: "[0-9]{13,16}"
        classification: "restricted"
    actions:
    restricted:
        - encrypt
        - log_access
        - require_approval

1. End-to-End Encryption for Sensitive Data
   • Example: Implement envelope encryption for all data with regularly rotated keys stored in a hardware security module (HSM).
   • Best Practice: Use AES-256-GCM for data encryption with key rotation every 90 days, with keys protected by an HSM.

# Example encryption configuration
encryption:
    algorithm: "AES-256-GCM"
    key_rotation_days: 90
    hsm:
    enabled: true
    provider: "aws"  # or "azure", "gcp", "on-prem"
    key_id: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

1. Data Minimization and Tokenization
   • Example: Replace sensitive data with tokens in all non-production environments and implement field-level masking for production queries.
   • Best Practice: Use format-preserving tokenization that maintains the data format but replaces sensitive values with non-sensitive equivalents.

# Example tokenization configuration
tokenization:
    enabled: true
    preserve_format: true
    fields:
    - name: "social_security_number"
        pattern: "XXX-XX-XXXX"
    - name: "credit_card_number"
        pattern: "XXXX-XXXX-XXXX-XXXX"
        expose_last: 4

3. Containerization Security

Defense Industrial Base (DIB) Level Security:

1. Hardened Container Images
   • Example: Build minimal containers using distroless or Alpine-based images, removing all unnecessary tools and libraries.
   • Best Practice: Implement mandatory vulnerability scanning with remediation SLAs based on severity (Critical: 24 hours, High: 7 days).

# Example Dockerfile with security hardening
FROM alpine:3.15 AS builder
# Build stages and dependencies here

FROM scratch
COPY --from=builder /app/binary /app/binary
USER 10001
WORKDIR /app
ENTRYPOINT ["/app/binary"]

1. Runtime Protection with Enhanced Isolation
   • Example: Implement pod security policies that enforce non-root execution, read-only file systems, and drop all capabilities except those specifically required.
   • Best Practice: Use gVisor or similar container runtime sandbox technologies to provide additional isolation between containers.

# Example Kubernetes pod security context
securityContext:
    runAsNonRoot: true
    runAsUser: 10001
    readOnlyRootFilesystem: true
    allowPrivilegeEscalation: false
    capabilities:
    drop: ["ALL"]
    add: ["NET_BIND_SERVICE"]

1. Continuous Compliance Monitoring
   • Example: Implement automated security scanning that validates container configurations against CIS benchmarks and NIST 800-171 controls.
   • Best Practice: Deploy continuous monitoring that automatically remediates or quarantines non-compliant resources.

# Example compliance monitoring configuration
compliance:
    frameworks:
    - name: "CMMC_2_0_LEVEL_2"
        enabled: true
    - name: "SOC2_TYPE_II"
        enabled: true
    - name: "NIST_800_171"
        enabled: true
    scanning:
    schedule: "0 */6 * * *"  # Every 6 hours
    automatic_remediation: true
    failure_policy: "quarantine"

4. Audit and Monitoring

Comprehensive Audit-Ready Trails:

1. Immutable Audit Logging
   • Example: Implement append-only logs stored in a tamper-proof system with cryptographic verification of log integrity.
   • Best Practice: Use a dedicated WORM (Write Once Read Many) storage solution with cryptographic signatures for each log entry.

# Example audit logging configuration
audit:
    enabled: true
    storage:
    type: "worm"
    retention_days: 365
    events:
    - category: "authentication"
        level: "info"
    - category: "authorization"
        level: "info"
    - category: "data_access"
        level: "info"
    tamper_proof:
    enabled: true
    signing_key: "/path/to/signing_key.pem"

1. Real-Time Threat Detection
   • Example: Deploy anomaly detection that identifies unusual access patterns or data transfer volumes and alerts security teams in real-time.
   • Best Practice: Implement behavioral analytics that establish baselines for normal user and system behavior and detect deviations.

# Example threat detection configuration
threat_detection:
    enabled: true
    baseline_learning_days: 30
    alerting:
    channels:
        - type: "email"
        recipients: ["security@example.com"]
        - type: "webhook"
        url: "https://security.example.com/alerts"
    thresholds:
        high: 0
        medium: 1
        low: 24  # hours before alert

1. Comprehensive Monitoring for Compliance
   • Example: Implement a monitoring system that correlates events across all MCP components to provide a unified view of system security.
   • Best Practice: Deploy a SIEM (Security Information and Event Management) system that ingests logs from all components and provides compliance-specific dashboards and reports.

# Example monitoring configuration
monitoring:
    metrics:
    enabled: true
    retention_days: 90
    dashboards:
    - name: "CMMC_Compliance"
        refresh_interval: "5m"
    - name: "SOC2_Controls"
        refresh_interval: "5m"
    alerts:
    - name: "Failed_Authentication"
        query: 'count(authentication_failure) > 5'
        interval: "5m"
        severity: "high"

5. Network Segmentation and Zero Trust

Defense-in-Depth Network Security:

1. Micro-Segmentation with Zero Trust
   • Example: Implement network policies that default to deny all traffic between services unless explicitly allowed, with segmentation based on workload identity rather than network location.
   • Best Practice: Use identity-based micro-segmentation that authenticates and authorizes every connection attempt regardless of source or destination.

# Example network policy
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
    name: default-deny-all
spec:
    podSelector: {}
    policyTypes:
    - Ingress
    - Egress
---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
    name: allow-specific-communication
spec:
    podSelector:
    matchLabels:
        app: frontend
    ingress:
    - from:
    - podSelector:
        matchLabels:
            app: api-gateway
    egress:
    - to:
    - podSelector:
        matchLabels:
            app: backend-service

1. Encrypted Data in Transit
   • Example: Enforce TLS 1.3 for all communications with strong cipher suites and certificate-based authentication.
   • Best Practice: Implement a service mesh like Istio to provide transparent mutual TLS for all service-to-service communication.

# Example service mesh mTLS configuration
apiVersion: security.istio.io/v1beta1
kind: PeerAuthentication
metadata:
    name: default
    namespace: istio-system
spec:
    mtls:
    mode: STRICT

1. Network Traffic Monitoring and Analysis
   • Example: Deploy a network monitoring solution that captures and analyzes all traffic between MCP components for security anomalies.
   • Best Practice: Implement deep packet inspection for unencrypted traffic and metadata analysis for encrypted traffic to detect potential threats.

# Example network monitoring configuration
network_monitoring:
    enabled: true
    packet_capture:
    enabled: true
    retention_days: 30
    flow_logs:
    enabled: true
    retention_days: 90
    anomaly_detection:
    enabled: true
    baseline_learning_days: 30

6. Continuous Security Validation

Proactive Security Assurance:

1. Automated Security Testing
   • Example: Implement scheduled penetration testing and vulnerability scanning that simulates attacker behavior.
   • Best Practice: Combine automated scanning with periodic manual penetration testing by certified security professionals.

# Example security testing configuration
security_testing:
    vulnerability_scanning:
    schedule: "0 0 * * 0"  # Weekly on Sunday
    scan_types:
        - network
        - container
        - code
    penetration_testing:
    schedule: "0 0 1 */3 *"  # Every 3 months
    notification:
        - security@example.com
        - compliance@example.com

1. Compliance Validation Automation
   • Example: Deploy automated tools that continuously validate system configurations against CMMC and SOC2 requirements.
   • Best Practice: Integrate compliance testing into CI/CD pipelines to prevent deployment of non-compliant configurations.

# Example CI/CD compliance check
compliance_check:
    enabled: true
    frameworks:
    - cmmc_2_level_2
    - soc2_type_2
    fail_on_violation: true
    report_path: "./compliance-reports/"

1. Incident Response Automation
   • Example: Implement automated incident response that can isolate affected components and initiate remediation workflows.
   • Best Practice: Develop and regularly test playbooks for common security incidents with both automated and manual response components.

# Example incident response configuration
incident_response:
    automatic_containment: true
    playbooks:
    - name: "credential_compromise"
        actions:
        - revoke_sessions
        - reset_credentials
        - notify_security_team
    - name: "data_exfiltration"
        actions:
        - block_ip
        - isolate_container
        - snapshot_evidence
        - notify_security_team

Implementation Best Practices for Compliance

1. Documentation and Policy Management
   • Maintain comprehensive documentation of all security controls and policies
   • Implement automated policy enforcement with regular reviews and updates
   • Create a dedicated compliance dashboard for real-time visibility
2. Vendor Risk Management
   • Assess and document security posture of all third-party components
   • Implement contract language requiring vendors to maintain appropriate security controls
   • Regularly review and validate vendor compliance
3. Employee Training and Awareness
   • Conduct regular security awareness training for all personnel
   • Implement role-specific security training for staff managing sensitive systems
   • Use phishing simulations and other assessment tools to validate training effectiveness
4. Configuration Management and Change Control
   • Implement infrastructure as code (IaC) with security validations
   • Require peer review for all configuration changes
   • Maintain a detailed inventory of all system components and configurations

By implementing these security considerations in your MCP deployment, you can achieve compliance with rigorous standards like CMMC 2.0 Level 2 and SOC2 Type II while ensuring the security of your sensitive data. These measures provide defense-in-depth protection for your containerized MCP environment and establish a strong foundation for ongoing security operations and compliance.

Detailed Integration with Specific Technologies

Neo4j Integration

Neo4j is a powerful graph database that can be seamlessly integrated with MCP to provide graph-based knowledge representations for AI models.

Implementation Approaches:

1. Direct Neo4j MCP Server:
   • There are multiple Neo4j MCP server implementations available, including official and community options.
   • Neo4j-specific MCP servers like neo4j-mcp and mcp-neo4j provide direct integration capabilities.
   • These servers expose graph database operations through the standardized MCP protocol.

Configuration Example:

services:
  neo4j:
    image: neo4j:latest
    environment:
      - NEO4J_AUTH=neo4j/your_password
      - NEO4J_dbms_memory_heap_max__size=4G
    ports:
      - "7474:7474"  # HTTP
      - "7687:7687"  # Bolt
    volumes:
      - neo4j-data:/data

  neo4j-mcp-server:
    image: your-neo4j-mcp-image
    environment:
      - NEO4J_URI=neo4j://neo4j:7687
      - NEO4J_USER=neo4j
      - NEO4J_PASSWORD=your_password
    depends_on:
      - neo4j

Key Features:

• Natural language to Cypher query translation
• Schema introspection capabilities
• Graph-based memory for AI systems
• Reading/writing operations on graph data
• Support for both Neo4j Cloud and self-hosted instances

Neo4j MCP servers enable AI models to understand complex relationships in data, making them particularly valuable for domain-specific knowledge bases, relationship mapping, and context-aware reasoning.

PostgreSQL Integration

PostgreSQL integration with MCP provides AI systems with access to structured relational data.

Implementation Options:

1. Official PostgreSQL MCP Server:
   • The official PostgreSQL MCP server provides read-only access with schema inspection capabilities.
   • This server allows AI models to understand database structure and query data effectively.
2. Custom PostgreSQL MCP Solutions:
   • Build custom MCP servers that connect to your PostgreSQL databases.
   • Implement natural language to SQL translation for more intuitive interactions.

Configuration Example:

services:
  postgres:
    image: postgres:14
    environment:
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=password
      - POSTGRES_DB=your_database
    volumes:
      - postgres-data:/var/lib/postgresql/data
    ports:
      - "5432:5432"

  postgres-mcp-server:
    image: mcp-postgres-server
    environment:
      - DB_CONNECTION_STRING=postgresql://user:password@postgres:5432/your_database
    depends_on:
      - postgres

Key Capabilities:

• Database schema introspection
• Natural language query translation to SQL
• Data visualization capabilities
• Secure access control

PostgreSQL MCP servers enable AI systems to interact with operational databases, business intelligence systems, and structured data stores without requiring direct database credentials or complex integration code.

HuggingFace Integration

HuggingFace is a hub for machine learning models, datasets, and spaces that can be integrated with MCP to provide AI systems with access to a wide range of capabilities.

Implementation Options:

1. HuggingFace MCP Server:
   • The HuggingFace MCP server provides read-only access to the HuggingFace Hub APIs.
   • This enables AI models to interact with HuggingFace’s models, datasets, spaces, papers, and collections.

Configuration Example:

services:
  huggingface-mcp-server:
    image: huggingface-mcp-server
    environment:
      - HF_API_TOKEN=your_huggingface_token  # Optional for private models/datasets

Key Features:

• Access to machine learning models
• Dataset discovery and exploration
• Integration with HuggingFace Spaces
• Model comparison and selection capabilities

HuggingFace MCP integration allows AI systems to leverage specialized models for specific tasks, search relevant datasets, and utilize broader AI ecosystem capabilities without duplicating model weights or complex setup procedures.

Next.js Frontend Integration

Next.js provides an excellent framework for building MCP Host interfaces that can interact with MCP servers.

Implementation Options:

1. Next.js as MCP Host:
   • Build a Next.js application that serves as the user interface for your AI system.
   • Implement MCP client functionality to connect to your MCP servers.
2. API Routes as Intermediaries:
   • Use Next.js API routes to handle communication between the frontend and MCP servers.
   • Implement authentication and access control at this layer.

Example Integration Architecture:

services:
  nextjs-app:
    build:
      context: ./frontend
      dockerfile: Dockerfile
    ports:
      - "3000:3000"
    environment:
      - MCP_CLIENT_CONFIG_PATH=/app/mcp-config.json
    volumes:
      - ./mcp-config.json:/app/mcp-config.json

Key Benefits:

• Modern, responsive user interfaces
• Server-side rendering capabilities
• API route intermediation for security
• Seamless deployment options

Next.js provides an ideal frontend framework for building AI applications that leverage MCP, offering both excellent developer experience and powerful user interfaces.

Third-Party Tool Integration

Atlassian Tools (Jira, Confluence, Trello)

The Atlassian suite of tools can be integrated with MCP to provide AI systems with access to project management, documentation, and collaboration data.

Implementation Options:

1. Atlassian MCP Server:
   • Use the mcp-atlassian server to connect to Jira and Confluence.
   • Configure authentication using API tokens or personal access tokens.

Configuration Example:

services:
  atlassian-mcp-server:
    image: mcp-atlassian
    environment:
      - CONFLUENCE_URL=https://your-company.atlassian.net/wiki
      - CONFLUENCE_USERNAME=your.email@company.com
      - CONFLUENCE_TOKEN=your_api_token
      - JIRA_URL=https://your-company.atlassian.net
      - JIRA_USERNAME=your.email@company.com
      - JIRA_TOKEN=your_api_token

Use Cases:

• Project management automation (Jira)
• Knowledge base access and search (Confluence)
• Task management integration (Trello)
• Automated documentation generation

Google Drive and Microsoft OneDrive

Cloud storage platforms can be integrated with MCP to provide AI systems with access to documents and files.

Implementation Options:

1. Google Drive MCP Server:
   • Use the official or community Google Drive MCP servers.
   • Set up OAuth authentication for secure access.
2. OneDrive API Integration:
   • Implement a custom MCP server that connects to the OneDrive API.
   • Configure authentication using Microsoft Graph API.

Configuration Example (Google Drive):

services:
  gdrive-mcp-server:
    image: mcp-gdrive
    environment:
      - CLIENT_ID=your_google_client_id
      - CLIENT_SECRET=your_google_client_secret
      - GDRIVE_CREDS_DIR=/app/creds
    volumes:
      - ./gdrive-creds:/app/creds

Use Cases:

• Document retrieval and analysis
• File search and categorization
• Collaborative document generation
• Content summarization and extraction

Xero Accounting

For financial data integration, Xero can be connected through custom MCP servers or through intermediary services.

Implementation Options:

1. Custom Xero MCP Server:
   • Build a custom MCP server that connects to the Xero API.
   • Implement authentication using OAuth 2.0.
2. Integration through Intermediary Services:
   • Use services like Zapier as intermediaries between MCP and Xero.

Example Use Cases:

• Financial data analysis
• Invoice processing and generation
• Expense categorization
• Financial reporting

Conclusion

Implementing the Model Context Protocol with local models using Docker/Podman provides a flexible, secure architecture for integrating AI capabilities with local data assets. By following the standardized protocol, you can create a modular system that can be extended with new data sources and capabilities while maintaining data privacy and security.

The integration capabilities with technologies like Neo4j, PostgreSQL, HuggingFace, and Next.js, as well as third-party tools like Atlassian products, Google Drive, Microsoft OneDrive, and Xero, demonstrate the versatility of the MCP approach. This enables you to build comprehensive AI systems that can access and manipulate data across a wide variety of sources while maintaining control over your infrastructure and data privacy.

This combined approach is particularly valuable for organizations that need to leverage AI capabilities while keeping sensitive data secure and maintaining compliance with data protection regulations. The containerized deployment model ensures scalability and portability, making it suitable for environments ranging from individual development setups to enterprise-scale deployments.