IaC Memory MCP Server

The IaC Memory MCP Server tackles a common pain point for teams that rely on AI assistants to reason about infrastructure-as-code (IaC): the lack of persistent, version‑aware context. Traditional AI models treat every query as a fresh conversation and cannot keep track of the evolving state of Terraform providers, Ansible collections, or custom infrastructure entities. This server fills that gap by acting as a long‑lived knowledge base that stores IaC components, their schemas, and the relationships between them. Developers can therefore ask Claude (or any MCP‑compatible assistant) for up‑to‑date information about a provider, discover all resources exposed by a module, or trace how a particular resource interacts with others across different versions—all without leaving the AI workflow.

At its core, the server exposes a hierarchical URI namespace () that organizes IaC artifacts in a predictable way. A Terraform provider, for example, is accessible via , while an Ansible module lives at . This structure allows the assistant to resolve references quickly and ensures that clients can navigate the resource tree programmatically. The server also maintains temporal metadata and schema validation, so each artifact carries its own version history and structural guarantees. When a provider is updated, the server records the new schema and documentation, enabling the assistant to surface differences or deprecations on demand.

The toolset is rich with actionable commands that mirror common IaC operations. Terraform‑centric tools let users add new providers, list available resources, or fetch detailed schema information for a specific resource type. Ansible tools perform analogous tasks—retrieving collection metadata, listing modules, or inspecting version histories. These operations are exposed as MCP tools, meaning they can be invoked directly from an AI prompt or chained with other tools to build sophisticated automation pipelines. For instance, a user might ask the assistant to “list all S3 bucket resources for AWS provider version 4.0” and receive a structured response that can be fed into another tool to generate deployment scripts.

Real‑world use cases include continuous integration pipelines where an AI assistant automatically verifies that the IaC used in a pull request is compatible with the latest provider version, or knowledge‑base maintenance where DevOps engineers keep a living document of all infrastructure components. Because the server tracks relationships, it can also highlight dependencies—such as which modules rely on a particular provider version—helping teams anticipate breaking changes. The persistent memory model means that context is retained across sessions, reducing repetitive explanations and allowing the assistant to build deeper, more accurate understanding over time.

What sets this MCP server apart is its focus on version tracking and relationship mapping. While many IaC tools expose static schemas, this server continuously records changes and links artifacts together, giving AI assistants a nuanced view of the infrastructure landscape. Developers benefit from having a single, authoritative source that feeds into their AI workflows, enabling more confident decision‑making, faster onboarding of new team members, and a smoother integration between human intent and automated IaC management.