A collection of Model Context Protocol (MCP) servers by IBM, to connect your IBM products to any AI Agent or AI application.
MCP is an open-source protocol designed to enable AI models to securely interact with local and remote resources through standardized server implementations. This collection of IBM MCP services focuses on both production-ready and experimental MCP servers that enhance AI capabilities by providing file access, database connections, API integrations, and additional contextual services.
The Terraform MCP Server provides seamless integration with Terraform ecosystem, enabling advanced automation and interaction capabilities for Infrastructure as Code (IaC) development. The use cases include:
- Automating Terraform provider, policy and module discovery
- Extracting detailed information about provider resources, data sources, user guides and functions
- Exploring and utilizing Terraform modules
Any GraphQL endpoint server by API Connect for GraphQL SaaS also supports an MCP stateless HTTP server supporting tools/list
and tools/call
.
Tools can be defined within a GraphQL schema using @tool
directive.
A GraphQL tool is a subset of the schema that the LLM generates a valid GraphQL request against:
ContextForge MCP Gateway is an MCP server, feature-rich gateway, and proxy that federates MCP and REST services-delivering unified discovery, authentication, rate-limiting, observability, multi-transport protocols, and an optional HTMX-powered admin UI through a single endpoint; It lets you create Virtual MCP Servers combining tools and resources across multiple servers, and it can be installed via PyPI (pip install mcp-contextforge-gateway
) or deployed as a cli, API or UI as a container - and scales horizontally across Kubernetes clusters with Redis-backed federation and caching.
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