semvec/pypi-packages huggingface dataset:
ambientmeta 0.1.0
Python SDK for the AmbientMeta Privacy Gateway — sanitize PII before LLM calls
License: MIT
Requires Python: >=3.9
Keywords: pii, privacy, llm, sanitize, redaction

# ambientmeta

Python SDK for the AmbientMeta Privacy Gateway — strip PII before sending text to any LLM, restore it after.

## Install

```bash
pip install ambientmeta
```

## Usage

```python
from ambientmeta import AmbientMeta

client = AmbientMeta(api_key="your-api-key")

# 1. Sanitize — strip PII before the LLM call
result = client.sanitize("Email John Smith at john@acme.com about the merger")

print(result.sanitized)
# "Email [PERSON_1] at [EMAIL_1] about the merger"

# 2. Send sanitized text to any LLM
llm_response = your_llm.complete(result.sanitized)

# 3. Rehydrate — restore PII after the LLM responds
restored = client.rehydrate(text=llm_response, session_id=result.session_id)
print(restored.text)
# Original PII restored in the LLM's response
```

## Async

```python
from ambientmeta import AsyncAmbientMeta

async with AsyncAmbientMeta(api_key="your-api-key") as client:
    result = await client.sanitize("Email John Smith at john@acme.com")
    restored = await client.rehydrate(text=llm_response, session_id=result.session_id)
```

## Error handling

```python
from ambientmeta import AmbientMetaError, AuthenticationError, RateLimitError

try:
    result = client.sanitize("text")
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Too many requests — wait and retry")
except AmbientMetaError as e:
    print(f"API error: {e.code} — {e.message}")
```

## Links

- [Quickstart guide](https://github.com/AmbientMeta/ambientmeta-api/blob/main/docs/quickstart.md)
- [API reference](https://github.com/AmbientMeta/ambientmeta-api/blob/main/docs/api-reference.md)
armor-mcp 0.2.1
AnomalyArmor MCP Server - Data observability tools for AI assistants
License: MIT
Requires Python: >=3.11
Keywords: ai, anomalyarmor, claude, data-observability, mcp

# armor-mcp

AnomalyArmor MCP Server - Data observability tools for AI assistants.

## Installation

```bash
# Via uvx (recommended)
uvx armor-mcp

# Or pip install
pip install armor-mcp
```

## Configuration

Set your API key:

```bash
export ARMOR_API_KEY=aa_live_your_key_here
```

Or create `~/.armor/config.yaml`:

```yaml
api_key: aa_live_your_key_here
```

## Usage

### With Claude Code

Add to your MCP configuration:

```json
{
  "mcpServers": {
    "armor": {
      "command": "uvx",
      "args": ["armor-mcp"]
    }
  }
}
```

### With Cursor

Add to `.cursor/mcp.json`:

```json
{
  "mcpServers": {
    "armor": {
      "command": "uvx",
      "args": ["armor-mcp"]
    }
  }
}
```

### Standalone

```bash
armor-mcp
```

## Available Tools

- `health_summary` - Overall data health status
- `list_alerts` - Query alerts with filtering
- `get_alert_summary` - Alert counts
- `list_assets` - List monitored assets
- `get_asset` - Get asset details
- `create_asset` - Create new data source
- `test_asset_connection` - Test connection
- `trigger_asset_discovery` - Start schema discovery
- `get_freshness_summary` - Freshness overview
- `check_freshness` - Check asset freshness
- `list_stale_assets` - List stale assets
- `list_freshness_schedules` - List schedules
- `create_freshness_schedule` - Create schedule
- `delete_freshness_schedule` - Delete schedule
- `get_schema_summary` - Schema drift overview
- `list_schema_changes` - List schema changes
- `create_schema_baseline` - Create baseline
- `enable_schema_monitoring` - Enable monitoring
- `disable_schema_monitoring` - Disable monitoring
- `ask_question` - Natural language Q&A
- `generate_intelligence` - Trigger AI analysis
- `get_lineage` - Get data lineage
- `job_status` - Check async job status

## License

MIT