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README
ATLAS-GATE-MCP
Enterprise Model Context Protocol Security Gateway with MCP-Only Sandbox Enforcement
High-Level Overview
ATLAS-GATE-MCP is a production-grade Model Context Protocol (MCP) server that implements enterprise-grade security governance, process-level sandbox enforcement, and comprehensive audit trails for AI agent operations. The system transforms unconstrained AI agents into governed execution authorities through a multi-layered security architecture that enforces zero-trust principles, plan-based authorization, and immutable audit logging.
The system addresses the fundamental security challenge of allowing AI agents to interact with critical systems while maintaining enterprise-grade security posture, compliance requirements, and operational visibility. ATLAS-GATE-MCP serves as a security gateway that mediates all AI agent interactions with development environments, enforcing strict governance policies while providing the flexibility required for modern AI-assisted development workflows.
ATLAS-GATE-MCP operates as an MCP server/gateway in modern AI infrastructure, sitting between AI models/clients and target systems (filesystems, databases, APIs, development tools). It is designed for enterprise environments where security, auditability, and compliance are non-negotiable requirements.
MCP Fundamentals
The Model Context Protocol (MCP) is a standardized protocol for enabling AI models to securely interact with external systems, tools, and data sources. MCP provides a structured framework for AI agent operations that addresses the limitations of traditional integration approaches.
Core Concepts and Terminology
- MCP Server: A process that exposes capabilities to AI models through standardized interfaces. ATLAS-GATE-MCP is an MCP server.
- Tools: Executable functions that AI models can invoke. Tools have defined schemas, parameters, and return values. Examples include file operations, API calls, database queries.
- Resources: Read-only data sources that AI models can access. Resources represent static or dynamic data like configuration files, documentation, or system state.
- Prompts: Reusable prompt templates that guide AI model behavior for specific tasks or contexts.
- Transport Layer: Communication mechanism between AI models and MCP servers, typically JSON-RPC 2.0 over stdio or HTTP.
- Session: A bounded interaction context between an AI model and MCP server with associated state and permissions.
Design Philosophy
MCP exists to solve fundamental architectural problems in AI agent integration:
- Security Isolation: Prevents AI models from having unrestricted system access
- Standardization: Provides consistent interfaces across different AI models and tools
- Governance: Enables policy enforcement and audit trails for AI operations
- Composability: Allows tools and capabilities to be combined safely
- Observability: Provides visibility into AI agent operations and decision-making
Compared to traditional APIs, MCP offers:
- Schema Validation: All tool inputs/outputs are validated against defined schemas
- Capability Discovery: AI models can discover available capabilities dynamically
- Security Boundaries: Built-in authentication, authorization, and audit mechanisms
- State Management: Proper session handling and state isolation
- Error Handling: Structured error responses with appropriate context
Compared to plugin-based systems, MCP provides:
- Protocol Standardization: Consistent interface across different implementations
- Security Model: Built-in security considerations rather than afterthought
- Transport Independence: Multiple communication patterns supported
- AI-First Design: Designed specifically for AI model interaction patterns
Atlas-Gate Architecture
ATLAS-GATE-MCP implements MCP through a multi-layered security architecture with strict separation of concerns and comprehensive enforcement mechanisms.
Core Components
-
MCP Server Core (
server.js): Central request handling, tool registration, and session management. Implements JSON-RPC 2.0 transport, input normalization, and request routing. -
Dual-Role System: Two distinct operational modes with different security boundaries:
- Windsurf Role: Execution and mutation capabilities with full audit logging
- Antigravity Role: Read-only analysis and planning capabilities
-
Tool Registry: 17 specialized tools covering file operations, plan management, audit logging, and attestation services. Each tool implements comprehensive validation and enforcement.
-
Governance Engine: Multi-layer policy enforcement including:
- Input validation with Zod schemas
- Plan-based authorization checking
- Static analysis for forbidden patterns
- Content integrity verification
- Audit trail generation
-
Security Layers:
- Process-level sandbox enforcement
- MCP-only operation restrictions
- Session-based isolation
- Cryptographic audit trails
- Role-based access control
Trust and Authorization Boundaries
- Trust Boundary 1: Process isolation through MCP sandbox prevents direct system access
- Trust Boundary 2: Role separation ensures read-only operations cannot mutate data
- Trust Boundary 3: Plan authorization requires explicit approval for operations
- Trust Boundary 4: Content validation prevents injection of malicious code
- Trust Boundary 5: Audit logging provides non-repudiation and traceability
Component Security Responsibilities
- Server Core: Request validation, session management, transport security
- Tool Handlers: Input validation, business logic enforcement, audit generation
- Governance Engine: Policy enforcement, content analysis, authorization checking
- Audit System: Immutable logging, tamper detection, compliance reporting
- Sandbox Layer: Process isolation, resource restriction, capability limiting
Architecture Diagram
┌─────────────────────────────────────────────────────────────────────────────────┐
│ AI Models / Clients │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Claude │ │ Windsurf │ │ Antigravity│ │ Custom LLM │ │
│ │ Desktop │ │ IDE │ │ Analysis │ │ Integration │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────┘
│
JSON-RPC 2.0 over stdio/HTTP
│
┌─────────────────────────────────────────────────────────────────────────────────┐
│ ATLAS-GATE-MCP SERVER │
│ ┌─────────────────────────────────────────────────────────────────────────────┐ │
│ │ MCP Transport Layer │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │
│ │ │ Request │ │ Session │ │ Response │ │ Error │ │ │
│ │ │ Normalizer │ │ Management │ │ Formatting │ │ Handling │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────────────────────────────┐ │
│ │ Security & Governance Layer │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │
│ │ │ Input │ │ Plan │ │ Content │ │ Audit │ │ │
│ │ │ Validation │ │ Authority │ │ Analysis │ │ Logging │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────────────────────────────┐ │
│ │ Tool Registry │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │
│ │ │ File Ops │ │ Plan Mgmt │ │ Audit │ │ Attestation │ │ │
│ │ │ (read/write)│ │ (list/lint) │ │ (log/replay)│ │ (gen/verify)│ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────────────┘ │
│ ┌─────────────────────────────────────────────────────────────────────────────┐ │
│ │ Role-Based Access │ │
│ │ ┌─────────────┐ ┌─────────────┐ │ │
│ │ │ Windsurf │ │ Antigravity│ │ │
│ │ │ (Execution) │ │ (Read-Only) │ │ │
│ │ └─────────────┘ └─────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────┘
│
MCP Protocol Interface
│
┌─────────────────────────────────────────────────────────────────────────────────┐
│ Backing Services & Systems │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Filesystem │ │ Plans Store │ │ Audit Log │ │ Database │ │
│ │ (Read/Write)│ │ (Authority) │ │ (Immutable) │ │ (Optional) │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────┘
Security Boundaries:
───────────────────────────────────────────────────────────────────────────────────
1. Process Sandbox: MCP-only execution, no direct system access
2. Role Separation: Windsurf (write) vs Antigravity (read-only)
3. Plan Authority: Operations require approved plan references
4. Content Validation: Static analysis prevents malicious code injection
5. Audit Trail: Immutable logging provides non-repudiation
How Atlas-Gate Works (Step-by-Step)
Initialization and Startup
- Process Launch: Server starts as either Windsurf or Antigravity role based on entry point
- Sandbox Activation: Process-level restrictions are applied via
lockdownProcess()andfreezeGlobalObjects() - Self-Audit: Server analyzes its own code for governance violations before accepting connections
- Session Generation: Cryptographic
SESSION_IDis generated for audit trail correlation - Transport Binding: JSON-RPC 2.0 server binds to stdio transport for MCP communication
- Tool Registration: All available tools are registered with their Zod schemas and handlers
- Capability Discovery: Server publishes available capabilities to connecting clients
Configuration and Capability Registration
- Role Determination: Server capabilities are filtered based on role (Windsurf vs Antigravity)
- Tool Schema Definition: Each tool defines input/output schemas using Zod for validation
- Permission Mapping: Tools are mapped to required permissions and authorization levels
- Plan Discovery: Available plans are scanned and cached for authorization checking
- Audit Configuration: Audit logging parameters and retention policies are established
Server Lifecycle and Request Handling
- Connection Acceptance: New JSON-RPC connections are accepted and authenticated
- Session Initialization:
begin_sessionmust be called to establish workspace context - Request Reception: JSON-RPC 2.0 requests are received via stdio transport
- Input Normalization: Raw inputs are normalized to structured objects
- Schema Validation: Request parameters are validated against tool schemas
- Authorization Checking: Plan-based authorization is verified before execution
- Content Analysis: Static analysis scans for forbidden patterns and security violations
- Execution: Business logic is executed with comprehensive error handling
- Audit Logging: All operations are logged with full context and cryptographic signatures
- Response Generation: Structured responses are formatted and returned
Model Discovery and Invocation of MCP Capabilities
- Capability Enumeration: AI models can list available tools and their schemas
- Tool Selection: Models select appropriate tools based on task requirements
- Parameter Preparation: Input parameters are prepared according to tool schemas
- Plan Reference: Operations must reference an approved plan for authorization
- Invocation: Tools are invoked via JSON-RPC 2.0 method calls
- Result Processing: Responses are parsed and integrated into model reasoning
- Error Handling: Structured error responses guide model recovery strategies
Request/Response Flow
- Request Reception: JSON-RPC 2.0 call received with method, parameters, and request ID
- Method Resolution: Appropriate tool handler is selected based on method name
- Parameter Extraction: Parameters are extracted and type-validated
- Context Establishment: Session context and workspace state are resolved
- Authorization Verification: Plan-based authorization is checked and validated
- Pre-execution Validation: Input sanitization and security scanning occur
- Business Logic Execution: Core tool functionality is executed with error handling
- Post-execution Processing: Results are formatted and audit entries are created
- Response Construction: JSON-RPC 2.0 response is built with result or error
- Transmission: Response is transmitted via stdio transport to client
State Handling and Isolation
- Session Isolation: Each session maintains independent state and permissions
- Workspace Binding: Operations are bound to specific workspace directories
- Plan Context: Authorization context is maintained throughout request lifecycle
- Audit Correlation: All operations are correlated via session and request IDs
- Stateless Design: Server does not maintain persistent state between requests
- Immutable Audit: Audit log provides the only persistent state management
Permission Evaluation and Enforcement
- Role-Based Access: Windsurf role allows mutations, Antigravity role is read-only
- Plan Authorization: Operations must reference valid, approved plans
- Capability Scoping: Tools have scoped permissions based on their function
- Content Restrictions: Static analysis prevents unauthorized code patterns
- Resource Limits: Filesystem access is limited to authorized directories
- Audit Requirements: All operations must pass audit logging requirements
- Failure Modes: Security violations result in immediate operation termination
Security and Permission Model
ATLAS-GATE-MCP implements a comprehensive, defense-in-depth security model designed for enterprise environments where security, compliance, and auditability are paramount.
Authentication Assumptions
ATLAS-GATE-MCP operates under a zero-trust security model with the following authentication assumptions:
Transport Security: Communication occurs over controlled stdio transport or authenticated HTTP channels. The protocol assumes the transport layer provides basic authentication and integrity protection.
Session Authentication: Sessions are established via begin_session with workspace binding and cryptographic session identifiers. Session state is maintained server-side with proper isolation.
Plan-Based Authentication: Operations are authenticated against approved plans stored in the governance repository. Plans serve as capability tokens that authorize specific operations within defined scopes.
Bootstrap Authentication: Initial system setup requires bootstrap secret authentication for creating the first governance plans. This secret is single-use and destroyed after initial configuration.
Authorization and Permission Boundaries
The authorization model implements multiple, overlapping permission boundaries:
Role-Based Authorization:
- Windsurf Role: Full execution capabilities including file writes, plan creation, and system modifications
- Antigravity Role: Read-only capabilities including file reads, plan inspection, and analysis operations
- Role Separation: Strict enforcement prevents privilege escalation between roles
Plan-Based Authorization:
- Plan Reference: All operations must reference an approved plan ID
- Scope Validation: Operations are validated against plan-defined scopes and constraints
- Hash Verification: Plan integrity is verified via cryptographic hashes
- Temporal Validity: Plans may include temporal constraints and expiration
Capability-Based Authorization:
- Tool Scoping: Each tool has defined capabilities and required permissions
- Parameter Validation: Tool parameters are validated against schemas and security policies
- Resource Access: Filesystem and resource access is limited to authorized paths
- Operation Constraints: Specific operations may have additional constraints (e.g., file size limits)
Access Control Mechanisms
MCP-Only Enforcement:
- Process sandbox prevents direct system access
- All operations must go through MCP tool interfaces
- Filesystem access is mediated through authorized tools only
- Network access is blocked by default with explicit allowances
Content Security:
- Static analysis prevents injection of malicious code patterns
- Stub detection prevents deployment of incomplete or placeholder code
- Policy enforcement blocks prohibited constructs and practices
- Content integrity verification ensures file consistency
Audit and Compliance:
- Comprehensive audit logging captures all operations with full context
- Cryptographic signatures ensure audit log integrity
- Tamper detection mechanisms prevent audit log manipulation
- Compliance reporting supports regulatory requirements
Data Exposure and Capability Escalation Prevention
Data Isolation:
- Session-based isolation prevents cross-session data leakage
- Workspace binding limits operations to authorized directories
- Path traversal protection prevents unauthorized file access
- Content filtering prevents sensitive data exposure in responses
Capability Escalation Prevention:
- Role separation prevents read-to-write privilege escalation
- Plan authorization prevents unauthorized capability acquisition
- Tool scoping limits operations to defined boundaries
- Static analysis prevents privilege escalation through code injection
Least Privilege Enforcement:
- Default-deny security posture with explicit authorization
- Minimal required permissions for each operation
- Time-limited authorization tokens where applicable
- Revocation capabilities for compromised sessions or plans
Design Principles
Zero Trust: Never trust, always verify. All operations require explicit authorization regardless of source.
Fail-Secure: Security violations result in operation termination rather than risky fallbacks.
Defense in Depth: Multiple, overlapping security controls ensure no single point of failure.
Audit by Design: All operations are auditable by design with comprehensive logging and integrity protection.
Least Privilege: Operations are granted minimal necessary permissions with scoped access.
Transparency: Security decisions are logged and auditable with clear reasoning and evidence.
MCP Capabilities in Atlas-Gate
ATLAS-GATE-MCP implements comprehensive MCP primitives with enterprise-grade security extensions.
Supported MCP Primitives
Tools: 17 specialized tools covering core operations:
- File Operations:
read_file,write_filewith comprehensive validation - Plan Management:
list_plans,lint_plan,bootstrap_toolfor governance - Audit Operations:
read_audit_log,replay_execution,verify_workspace_integrity - Attestation:
generate_attestation_bundle,verify_attestation_bundle,export_attestation_bundle - Session Management:
begin_session,read_promptfor session initialization
Resources: Read-only access to system state and configuration:
- Plan Resources: Approved governance plans and their metadata
- Audit Resources: Historical audit logs and compliance data
- Configuration Resources: System configuration and security policies
- Documentation Resources: System documentation and operational guides
Prompts: Reusable prompt templates for common operations:
- Planning Prompts: Templates for creating and validating governance plans
- Analysis Prompts: Templates for security analysis and compliance checking
- Operational Prompts: Templates for common operational tasks
Security Extensions
Enhanced Validation: All inputs undergo comprehensive security validation beyond standard MCP schema validation:
- Static analysis for security patterns
- Content integrity verification
- Policy compliance checking
- Threat pattern detection
Audit Integration: All operations are integrated with the audit system:
- Pre-execution audit logging
- Post-execution result capture
- Cryptographic signature generation
- Tamper-evident storage
Authorization Extensions: Plan-based authorization extends standard MCP capabilities:
- Capability token management
- Scope-based access control
- Temporal authorization constraints
- Revocation and renewal mechanisms
Extensibility and Interoperability
Tool Registration: New tools can be registered with comprehensive security validation:
- Schema definition and validation
- Security policy integration
- Audit logging integration
- Authorization mapping
Protocol Compatibility: Full MCP protocol compliance ensures interoperability:
- Standard JSON-RPC 2.0 transport
- Compliant tool and resource definitions
- Standard error handling and response formats
- Capability discovery mechanisms
Integration Points: Well-defined interfaces for system integration:
- Authentication provider interfaces
- Authorization system hooks
- Audit log consumers
- Monitoring and alerting endpoints
Differentiation from Other MCP Servers
Enterprise Security Focus: Unlike development-focused MCP servers, ATLAS-GATE-MCP prioritizes security, compliance, and auditability over flexibility.
Governance Integration: Built-in plan-based governance system provides structured authorization lacking in standard MCP implementations.
Comprehensive Auditing: Immutable, cryptographically-signed audit trails exceed standard MCP logging capabilities.
Process Isolation: MCP-only sandbox enforcement provides stronger isolation than typical MCP server implementations.
Static Analysis Integration: Real-time code analysis and security scanning are unique capabilities not found in standard MCP servers.
Usage Overview
Primary Use Cases
Enterprise AI Development: Secure AI-assisted development in regulated environments where code quality, security, and compliance are mandatory.
Compliance-Driven Development: Development workflows requiring comprehensive audit trails, change approval, and regulatory compliance documentation.
High-Security Environments: Development of security-critical systems where code injection, privilege escalation, and data leakage must be prevented.
Multi-Tenant Development: Shared development environments where strict isolation and auditability between teams and projects are required.
Governed AI Operations: Organizations requiring structured approval processes for AI-assisted operations with clear accountability and traceability.
Typical Deployment Scenarios
Development Gateway: ATLAS-GATE-MCP deployed as a gateway between AI development tools and code repositories, enforcing governance policies and maintaining audit trails.
Compliance Enforcement: Integration with compliance systems to ensure all AI-assisted development meets regulatory requirements and organizational policies.
Security Monitoring: Integration with security operations centers to monitor AI agent activities and detect potential security violations.
Audit and Forensics: Providing comprehensive audit trails for incident response, forensic analysis, and compliance reporting.
Target Users
Platform Engineers: Responsible for maintaining secure development infrastructure and ensuring compliance with organizational policies.
Security Engineers: Focused on preventing security vulnerabilities, maintaining audit trails, and ensuring secure AI operations.
Compliance Officers: Ensuring development activities meet regulatory requirements and organizational standards.
Development Teams: Using AI assistance while maintaining security, quality, and compliance standards.
DevOps Engineers: Integrating AI-assisted development into CI/CD pipelines with proper governance and monitoring.
When to Use Atlas-Gate
Use Atlas-Gate when:
- Enterprise-grade security is non-negotiable
- Comprehensive audit trails are required
- Regulatory compliance must be maintained
- Multiple teams share development resources
- AI assistance is needed but must be tightly controlled
- Code quality and security standards must be enforced
- Change approval processes are required
Consider alternatives when:
- Development speed is the primary concern over security
- Simple, single-developer projects without compliance requirements
- Experimental development where flexibility is more important than governance
- Organizations without established security and compliance frameworks
How Atlas-Gate Compares to Alternatives
Traditional REST/GraphQL APIs
Security Model:
- REST/GraphQL: Typically rely on network-level security (TLS, API keys) with application-level authorization
- Atlas-Gate: Multi-layer security with process isolation, content validation, and comprehensive audit trails
State Management:
- REST/GraphQL: Often stateless or require explicit session management
- Atlas-Gate: Built-in session management with cryptographic isolation and audit correlation
Schema Validation:
- REST/GraphQL: Schema validation varies by implementation; often optional
- Atlas-Gate: Mandatory schema validation with Zod and additional security scanning
AI Integration:
- REST/GraphQL: Not designed for AI model interaction patterns
- Atlas-Gate: Specifically designed for AI model interaction with appropriate error handling and response formats
Compliance:
- REST/GraphQL: Audit logging must be implemented manually
- Atlas-Gate: Comprehensive, immutable audit trails built into the protocol
Plugin-based LLM Systems
Security Boundaries:
- Plugin Systems: Often rely on plugin developer security practices; vulnerabilities can affect entire system
- Atlas-Gate: Process-level isolation prevents plugin vulnerabilities from affecting system security
Update Mechanisms:
- Plugin Systems: Dynamic loading can introduce security risks and version conflicts
- Atlas-Gate: Static tool registration with comprehensive validation prevents runtime security issues
Authorization:
- Plugin Systems: Authorization varies by plugin implementation
- Atlas-Gate: Consistent, plan-based authorization across all capabilities
Audit Trail:
- Plugin Systems: Audit logging is inconsistent across plugins
- Atlas-Gate: Uniform, comprehensive audit trail for all operations
Agent Frameworks with Embedded Tools
Tool Isolation:
- Agent Frameworks: Tools often run in same process as agent, increasing attack surface
- Atlas-Gate: Process isolation and sandbox enforcement provide stronger security boundaries
Governance:
- Agent Frameworks: Governance varies by implementation; often lacks enterprise-grade controls
- Atlas-Gate: Built-in governance system with plan-based authorization and compliance features
Scalability:
- Agent Frameworks: Often designed for single-agent scenarios
- Atlas-Gate: Designed for multi-agent, multi-session enterprise environments
Compliance:
- Agent Frameworks: Compliance features vary; often inadequate for regulated environments
- Atlas-Gate: Enterprise-grade compliance features with comprehensive audit trails
Other MCP Servers
Security Focus:
- Other MCP Servers: Often prioritize flexibility and ease of use over security
- Atlas-Gate: Security-first design with comprehensive enforcement mechanisms
Governance Integration:
- Other MCP Servers: Limited or no governance capabilities
- Atlas-Gate: Built-in plan-based governance system with approval workflows
Audit Capabilities:
- Other MCP Servers: Basic logging capabilities
- Atlas-Gate: Comprehensive, cryptographically-signed audit trails with tamper detection
Enterprise Features:
- Other MCP Servers: Often designed for development or experimental use
- Atlas-Gate: Designed specifically for enterprise production environments
Design Decisions and Tradeoffs
Key Architectural Choices
MCP-Only Sandbox Enforcement:
- Decision: Restrict all operations to MCP protocol interfaces
- Rationale: Prevents direct system access and ensures all operations are auditable
- Tradeoff: Reduced flexibility for significantly improved security posture
Plan-Based Authorization:
- Decision: Require all operations to reference approved governance plans
- Rationale: Provides structured authorization and change approval processes
- Tradeoff: Additional complexity for improved governance and compliance
Static Analysis Integration:
- Decision: Scan all content for security patterns before execution
- Rationale: Prevents injection of malicious code and policy violations
- Tradeoff: Performance overhead for enhanced security assurance
Immutable Audit Trails:
- Decision: Maintain cryptographically-signed, tamper-evident audit logs
- Rationale: Provides non-repudiation and compliance support
- Tradeoff: Storage overhead and complexity for audit integrity
Constraints and Limitations
Performance Considerations:
- Static analysis and content validation add latency to operations
- Comprehensive audit logging generates significant data volume
- Process isolation introduces communication overhead
Scalability Limitations:
- Single-server architecture limits horizontal scaling
- Session state management constrains concurrent operations
- File-based audit storage has performance implications
Operational Complexity:
- Plan governance requires administrative overhead
- Security configuration requires specialized knowledge
- Audit management and retention policies add operational burden
Flexibility Constraints:
- Strict security policies limit certain development patterns
- Plan-based authorization can slow rapid iteration
- MCP-only restriction prevents direct system integration
Non-Goals
General-Purpose Server: ATLAS-GATE-MCP is not designed to replace general-purpose application servers or API gateways.
Development Speed Optimization: The system prioritizes security and compliance over development velocity.
Multi-Protocol Support: Focus on MCP protocol rather than supporting multiple integration protocols.
Real-time Performance: Not optimized for high-frequency, low-latency operations typical of trading systems or real-time analytics.
Dynamic Configuration: Runtime reconfiguration is limited to maintain security guarantees.
Future Directions / Roadmap
Plausible Future Enhancements
Distributed Architecture: Multi-server deployment with load balancing and horizontal scaling capabilities.
Database Integration: Support for external databases for audit storage and session management.
Advanced Authentication: Integration with enterprise authentication systems (LDAP, SAML, OAuth2).
Policy Engine: Rule-based policy engine for more sophisticated authorization decisions.
Performance Optimization: Caching mechanisms and optimized static analysis for improved performance.
Monitoring Integration: Enhanced monitoring and alerting integration with enterprise observability platforms.
Compliance Automation: Automated compliance reporting and regulatory requirement mapping.
Areas of Exploration
Machine Learning Integration: ML-based anomaly detection for security threat identification.
Advanced Threat Protection: Integration with threat intelligence feeds and automated response capabilities.
Cross-Platform Support: Support for additional operating systems and deployment environments.
Protocol Extensions: Extensions to MCP protocol for enhanced security and governance features.
Community Ecosystem: Development of additional tools and integrations by the community.
Enterprise Features: Additional enterprise-specific features like role-based administration and advanced reporting.
Long-Term Direction
Standardization Leadership: Active participation in MCP protocol standardization and security best practices development.
Ecosystem Development: Growth of a partner ecosystem for integrations and complementary tools.
Compliance Expansion: Support for additional regulatory frameworks and compliance requirements.
Security Innovation: Continued innovation in AI security and governance mechanisms.
Performance Evolution: Ongoing performance optimization while maintaining security guarantees.
Glossary
MCP (Model Context Protocol): Standardized protocol for AI model interaction with external systems, tools, and data sources.
Atlas-Gate: Enterprise MCP server implementing security governance, sandbox enforcement, and comprehensive audit trails.
Windsurf Role: Atlas-Gate operational mode with execution and mutation capabilities, including file writes and system modifications.
Antigravity Role: Atlas-Gate operational mode with read-only capabilities for analysis and planning operations.
Plan: Governance document that defines authorization scopes, constraints, and approval requirements for operations.
Tool: Executable function exposed through MCP interface with defined schemas and security policies.
Resource: Read-only data source accessible through MCP interface with proper authorization.
Session: Bounded interaction context between AI model and Atlas-Gate server with associated state and permissions.
Audit Trail: Comprehensive, immutable log of all operations with cryptographic integrity protection.
Sandbox: Process-level isolation mechanism that restricts operations to MCP protocol interfaces only.
Static Analysis: Automated code scanning for security patterns, policy violations, and prohibited constructs.
Stub Detection: Identification of incomplete, placeholder, or non-production code patterns.
Authorization: Process of verifying that operations are permitted based on roles, plans, and security policies.
Governance: System of rules, policies, and procedures for controlling and monitoring AI agent operations.
Compliance: Adherence to regulatory requirements, organizational policies, and industry standards.
Zero Trust: Security model that assumes no implicit trust and requires verification for all operations.
Least Privilege: Security principle of granting minimal necessary permissions for required operations.
Non-repudiation: Assurance that operations cannot be denied by their performers, typically through audit trails.
Tamper-evident: Property that unauthorized modifications to data or systems can be detected.
Capability Token: Authorization credential that grants specific permissions within defined scopes.
Content Integrity: Assurance that content has not been altered in unauthorized ways.
Policy Enforcement: Automated application of security policies and governance rules.
Transport Layer: Communication mechanism between AI models and MCP servers, typically JSON-RPC 2.0.
Schema Validation: Verification that input data conforms to defined structure and type requirements.
Session Isolation: Separation of state and permissions between different concurrent sessions.
Workspace Binding: Association of operations with specific workspace directories for security and organization.
Cryptographic Signature: Digital signature used to verify authenticity and integrity of audit entries.
Threat Pattern: Recognized pattern of malicious or unauthorized activity that security systems should detect.
Enterprise Grade: Security and reliability standards suitable for large organizational deployment.
Production Ready: System state appropriate for live operational use with proper security, monitoring, and support.
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用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。