# io.github.dingdawg/dingdawg-data-agent (npm · dingdawg-data-agent)

AI data agent — schema analysis, query optimization, PII detection, pipelines.

- Trust score: 65/100 (medium)
- Change this week: +41
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-04

## Components

- npm · `dingdawg-data-agent`: 65/100 (this document), [markdown](https://verifymcp.io/servers/dingdawg-dingdawg-data-agent/dingdawg-data-agent.md), [page](https://verifymcp.io/servers/dingdawg-dingdawg-data-agent/dingdawg-data-agent)

## Channel facts

- Registry: `npm`
- Package: `dingdawg-data-agent`
- Version: `2.0.7`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-04.

- **Supply Chain Security**: 83/100
  - No malware found by supply-chain analysis.
  - CVE check failed: a known medium-severity CVE affects hono 4.12.33, reached via @modelcontextprotocol/sdk > hono. A fixed version is available.
  - No install/post-install scripts declared.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 27 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 57/100
  - AI-judged instruction clarity (fair).
  - Tool/resource definitions use about 329 tokens (~65/item across 5 items; 5 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 30/100
  - Stability observed for 9 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add dingdawg-dingdawg-data-agent -- npx -y dingdawg-data-agent
```

### Codex

```bash
codex mcp add dingdawg-dingdawg-data-agent -- npx -y dingdawg-data-agent
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "dingdawg-dingdawg-data-agent": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "dingdawg-data-agent"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add dingdawg-dingdawg-data-agent --command npx --arg -y --arg dingdawg-data-agent
```

### Hermes

```yaml
mcp_servers:
  dingdawg-dingdawg-data-agent:
    command: "npx"
    args: ["-y", "dingdawg-data-agent"]
```

### Other

```json
{
  "mcpServers": {
    "dingdawg-dingdawg-data-agent": {
      "command": "npx",
      "args": [
        "-y",
        "dingdawg-data-agent"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-04 (score 65, +4)

- [security regression] CVE-2026-69207 affects this package: medium
- [security regression] Known CVEs: partial → fail
- [functional improvement] Stability: unverified → 0.30

### 2026-08-02 (score 61, +35)

- [security regression] Provenance: unverified → fail
- [security improvement] Install scripts: unverified → pass
- [security improvement] Known CVEs: unverified → partial
- [security improvement] Malware scan: unverified → pass
- [security] Stability: Stability not yet verified: we do not have a sandbox capture of the MCP schema this version of the package serves yet.
- [functional regression] Capabilities: pass → unverified
- [functional regression] Tool coverage: 100 → unverified
- [functional improvement] Maintenance: unverified → pass
- [functional improvement] Schema quality: unverified → fair
- [functional improvement] License: unverified → pass
- [functional improvement] Dependency health: unverified → partial
- [functional] Licence: MIT

### 2026-08-01 (score 26, +5)

- [security] Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window).
- [functional improvement] MCP protocol: unverified → pass

### 2026-07-31 (score 21, −25)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 46, +22)

- [functional improvement] Tool coverage: unverified → 100

### 2026-07-28 (score 24, −22)

- [functional regression] Tool coverage: 100 → unverified
- [functional] First check of Schema quality: unverified

### 2026-07-27 (score 46)

First indexed and scored.

## MCP tools (5)

### `analyze_data` (~71 tokens)

Free AI data schema analysis. Returns quality score, PII detection, and recommendations. Deep LLM-powered analysis with API key.

Input parameters:

- `database` (string): Database type (postgres, mysql, sqlite, mongodb, etc.)
- `schema` (string, required): Dataset schema, DDL, or description of your data

### `query_builder` (~72 tokens)

Free AI SQL query guidance from natural language. Deep LLM-powered optimized SQL generation with API key.

Input parameters:

- `database` (string): Database type (postgres, mysql, sqlite)
- `question` (string, required): Natural language question to convert to SQL
- `schema_context` (string): Table/schema context for accurate query generation

### `pipeline_design` (~68 tokens)

AI-powered data pipeline architecture design. Stages, tool recommendations, scheduling, error handling. Requires DINGDAWG_API_KEY.

Input parameters:

- `destination` (string, required): Data destination / warehouse
- `requirements` (string): Volume, latency, transformation requirements
- `source` (string, required): Data source description

### `data_quality` (~51 tokens)

AI data quality checks with anomaly detection and fix recommendations. Requires DINGDAWG_API_KEY.

Input parameters:

- `dataset` (string, required): Dataset description or sample data
- `rules` (string): Custom quality rules to check

### `privacy_scan` (~67 tokens)

AI privacy scan for PII detection, GDPR/CCPA compliance, anonymization recommendations. Requires DINGDAWG_API_KEY.

Input parameters:

- `regulations` (string): Regulations to check (GDPR, CCPA, HIPAA)
- `schema` (string, required): Database schema or data description to scan

## Diagnostics

Captured diagnostic sections: Provenance, Vulnerabilities, Dependencies. The full working is on the page: https://verifymcp.io/servers/dingdawg-dingdawg-data-agent/dingdawg-data-agent#diagnostics

## Score history

- 2026-08-04: 65
- 2026-08-03: 61
- 2026-08-02: 61
- 2026-08-01: 26
- 2026-07-31: 21
- 2026-07-30: 46
- 2026-07-28: 24
- 2026-07-27: 46

## Links

- npm package: https://www.npmjs.com/package/dingdawg-data-agent
- Socket report: https://socket.dev/npm/package/dingdawg-data-agent
- Repository: https://github.com/dingdawg/dingdawg-agent-1
- Changelog RSS feed: https://verifymcp.io/servers/dingdawg-dingdawg-data-agent/dingdawg-data-agent/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/dingdawg-dingdawg-data-agent/dingdawg-data-agent/changelog.json
- HTML version of this page: https://verifymcp.io/servers/dingdawg-dingdawg-data-agent/dingdawg-data-agent