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Description
ContextForge MCP Gateway serves as an open-source platform that functions as a Model Context Protocol (MCP) gateway, registry, and proxy, offering a consolidated endpoint for artificial intelligence clients to find and utilize tools, resources, prompts, as well as REST or MCP services within intricate AI ecosystems. This solution operates in front of various MCP servers and REST APIs, facilitating federated and unified processes for discovery, authentication, rate-limiting, observability, and traffic management across numerous back-end systems, while accommodating multiple transport methods like HTTP, JSON-RPC, WebSocket, SSE, stdio, and streamable HTTP; it also has the capability to transform legacy APIs into MCP-compliant tools. Additionally, the platform features an optional Admin UI that enables users to configure, monitor, and access logs in real time, and it is architected to scale efficiently, from single-instance deployments to expansive multi-cluster Kubernetes setups, utilizing Redis for federation and caching to enhance both performance and resilience. In this way, the ContextForge MCP Gateway not only simplifies the interaction within complex AI architectures but also ensures robust functionality and adaptability across various operational environments.
Description
Security and observability tailored for Kubernetes environments. Implementing security and observability as code is essential for modern cloud-native applications. This approach encompasses cloud-native security as code for various elements, including hosts, virtual machines, containers, Kubernetes components, workloads, and services, ensuring protection for both north-south and east-west traffic while facilitating enterprise security measures and maintaining continuous compliance. Furthermore, Kubernetes-native observability as code allows for the gathering of real-time telemetry, enhanced with context from Kubernetes, offering a dynamic view of interactions among components from hosts to services. This enables swift troubleshooting through machine learning-driven detection of anomalies and performance issues. Utilizing a single framework, organizations can effectively secure, monitor, and address challenges in multi-cluster, multi-cloud, and hybrid-cloud environments operating on either Linux or Windows containers. With the ability to update and deploy security policies in mere seconds, businesses can promptly enforce compliance and address any emerging issues. This streamlined process is vital for maintaining the integrity and performance of cloud-native infrastructures.
API Access
Has API
API Access
Has API
Integrations
Kubernetes
Calico Cloud
Calico Enterprise
Claude
Docker
FortiADC
Jaeger
Model Context Protocol (MCP)
OpenAI
Phoenix
Integrations
Kubernetes
Calico Cloud
Calico Enterprise
Claude
Docker
FortiADC
Jaeger
Model Context Protocol (MCP)
OpenAI
Phoenix
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
IBM
Founded
1911
Country
United States
Website
ibm.github.io/mcp-context-forge/
Vendor Details
Company Name
Tigera
Country
United States
Website
www.tigera.io
Product Features
Product Features
Cloud Security
Antivirus
Application Security
Behavioral Analytics
Encryption
Endpoint Management
Incident Management
Intrusion Detection System
Threat Intelligence
Two-Factor Authentication
Vulnerability Management
Cloud Workload Protection
Anomaly Detection
Asset Discovery
Cloud Gap Analysis
Cloud Registry
Data Loss Prevention (DLP)
Data Security
Governance
Logging & Reporting
Machine Learning
Security Audit
Workload Diversity
Container Security
Access Roles / Permissions
Application Performance Tracking
Centralized Policy Management
Container Stack Scanning
Image Vulnerability Detection
Reporting
Testing
View Container Metadata
