semvec/pypi-packages huggingface dataset:
context-local-server 0.1.1
A stdio MCP server to provide long-term memory for AI agents.
License: MIT License
Requires Python: >=3.8

# AI Agent Context Server

**local-context-server** is a lightweight, local MCP (Model Context Protocol) server designed to provide long-term memory for AI agents like Gemini, Claude, and others within environments like Cursor.

It allows an AI agent to save, load, list, and search for *"contexts"*—pieces of knowledge such as application maps, test data, or business requirements—making it possible to create adaptive and intelligent QA automation.

## Features

- **Persistent Memory:** Store any JSON-serializable data in a local SQLite database.

- **Simple Tool API:** Provides four core tools for the AI to manage its knowledge:
  - `save_context`: Save or update a piece of knowledge.
  - `load_context`: Retrieve knowledge by its unique ID.
  - `list_contexts`: Browse all available knowledge.
  - `search_contexts`: Search for knowledge by keyword.

- **Automatic Database Location:** The server automatically creates and manages its database file (`memory_tests.db`) in a folder named `context-database` inside your user's home directory. This requires no configuration.

# Installation & Usage

The server is designed to be run on-the-fly by an MCP client like Cursor, requiring no manual setup for end-users.

## Prerequisites

Python 3.8+
pipx (a tool for running Python applications in isolated environments)


```Bash
pip install pipx
Usage in Cursor
````

To use this server with Cursor, add the following to your .cursor/mcp.json configuration file. This command downloads and runs the server package from PyPI.

```JSON
"mcpServers": {
    "context-local-server": {
      "command": "pipx",
      "args": [
        "run",
        "context-local-server"
      ]
    },
}
```

# How to Use with an AI Agent

Once configured in Cursor, you can interact with the server using the name you defined in mcp.json.

## Saving a Context
```
Your mission is to execute the login flow and document the steps. As you perform each action (navigate, enter text, click) with @mobile-mcp, add a description of that action to a list.

When you have successfully logged in, use @local-context-server to save the complete list of steps with the id flow_login_v1.
```

## Listing all Contexts

```
Use @local-context-server, execute list_contexts to show me all the knowledge you have.
```

## Searching for a Context

```
Your mission is to perform the login flow. I don't remember the exact ID. Use @local-context-server to search for contexts related to "login" (search by keyword "login" or related), load the correct one, and then execute the steps using @mobile-mcp.
```

## Using a Context

```
Perform a login using @mobile-mcp. Use the context app_map_v1 from @local-context-server as your knowledge base for the element IDs.
```