mcp-memory-server 1.3.0

MCP server that exposes Project AI Memory (memory.md) as resources and tools for Cursor, Claude, and other MCP clients

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

pip install mcp-memory-server

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

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

What it exposes

• Resource memory://project-context – full contents of memory.md
• Tools – get_memory_section, get_security_checklist, get_code_standards

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):

{
  "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:

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

Requires uv (curl -LsSf https://astral.sh/uv/install.sh | sh).

Publish to PyPI (so others can use uvx)

From this repo, after tests pass:

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