Metadata-Version: 2.4
Name: mcp-memory-server
Version: 1.4.0
Summary: MCP server that exposes Project AI Memory (memory.md) as resources and tools for Cursor, Claude, and other MCP clients
License-Expression: MIT
Project-URL: Homepage, https://github.com/NeetPatel/devops-mcp
Project-URL: Repository, https://github.com/NeetPatel/devops-mcp
Project-URL: Issues, https://github.com/NeetPatel/devops-mcp/issues
Keywords: mcp,model-context-protocol,cursor,ai,memory,devops,devsecops
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: mcp>=1.0.0
Requires-Dist: kubernetes>=28.0.0
Requires-Dist: docker>=7.0.0
Requires-Dist: psutil>=5.9.0
Provides-Extra: dev
Requires-Dist: build>=1.0; extra == "dev"
Requires-Dist: twine>=4.0; extra == "dev"
Provides-Extra: prometheus
Requires-Dist: prometheus-api-client>=0.5.0; extra == "prometheus"

mcp-name: io.github.NeetPatel/devops-mcp

# DevOps MCP Server

MCP server that exposes **Project AI Memory** (`memory.md`) as resources and tools so AI clients (Cursor, Claude Desktop, etc.) can load DevOps/DevSecOps context.

## Install

```bash
pip install mcp-memory-server
```

Run from a directory that contains `memory.md`, or set the path:

```bash
mcp-memory
# or
MCP_MEMORY_PATH=/path/to/memory.md mcp-memory
```

## What it exposes

**Memory (project policy):**
- **Resource** `memory://project-context` – full contents of `memory.md`
- **Tools** – `get_memory_section`, `get_security_checklist`, `get_code_standards`

**Execution tools (MCP tool names):**

| DevOps tool | MCP tool name     | Implementation |
|-------------|-------------------|----------------|
| Terraform   | `terraform.plan`  | CLI            |
| Kubernetes  | `k8s.pods`       | CLI (kubectl)  |
| Docker      | `docker.images`  | CLI            |
| GitHub      | `github.action`  | GitHub API     |
| Jenkins     | `jenkins.jobs`   | Jenkins API    |
| Jenkins     | `jenkins.build`  | Jenkins API    |
| Trivy       | `security.scan`  | CLI            |
| Registry    | `registry.images`| Registry V2 API |
| Drift       | `drift.check`    | Terraform CLI  |
| K8s drift   | `k8s.drift`      | kubectl diff   |
| DBA         | `dba.query`      | psql (CLI)     |

Install CLIs (kubectl, terraform, docker, trivy, psql, curl) via `bash install-devops-tools.sh`; see **TOOLS.md**. Env: `GITHUB_TOKEN`, `JENKINS_URL` (+ optional `JENKINS_USER`/`JENKINS_TOKEN`), `REGISTRY_URL` (+ optional `REGISTRY_USER`/`REGISTRY_TOKEN`) for private registries.

## Load in Cursor (any system, like AWS Labs MCPs)

Add to Cursor MCP config (`~/.cursor/mcp.json` or **Settings → MCP**). Uses **uvx** so the server runs from PyPI without a local clone (same pattern as `awslabs.cdk-mcp-server`):

```json
{
  "mcpServers": {
    "devops-mcp": {
      "command": "uvx",
      "args": ["mcp-memory-server@latest"],
      "env": {}
    }
  }
}
```

- Use `mcp-memory-server` for latest install, or `mcp-memory-server@latest` to pin to latest.
- Cursor uses your **workspace as cwd**, so put `memory.md` in the project root, or set a path in `env`:

```json
"devops-mcp": {
  "command": "uvx",
  "args": ["mcp-memory-server@latest"],
  "env": {
    "MCP_MEMORY_PATH": "/path/to/your/memory.md"
  }
}
```

Requires [uv](https://docs.astral.sh/uv/) (`curl -LsSf https://astral.sh/uv/install.sh | sh`).

## Publish to PyPI (so others can use uvx)

From this repo, after tests pass:

```bash
uv run python -m build
uv run twine upload dist/*
```

Or with a GitHub workflow: build the package, then `twine upload` using a PyPI token. Once `mcp-memory-server` is on PyPI, anyone can use the Cursor config above on any machine.

## Links

- Repository: https://github.com/NeetPatel/devops-mcp
