# Data Quality Gate - deterministic post-scrape cleaner + verdict (remote · www.aidatatools.dev)

Post-scrape data cleaner, no LLM: repairs mojibake, HTML, invisible chars. Plus a verdict.

- Trust score: 67/100 (medium)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-20

## Components

- remote · `www.aidatatools.dev`: 67/100 (this document), [markdown](https://verifymcp.io/servers/aidatatools-dev-data-quality-gate/api-mcp-server.md), [page](https://verifymcp.io/servers/aidatatools-dev-data-quality-gate/api-mcp-server)

## Channel facts

- Endpoint: `https://www.aidatatools.dev/api/mcp_server`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.3.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, 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 2026-08-20.

- **Endpoint Security**: 63/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: no authorisation is required to call this server, and 3 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 58/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 1324 tokens (~441/item across 3 items; 3 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 37/100
  - Stability observed for 11 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 current MCP spec version (2026-07-28).

## Install

### Claude

```bash
claude mcp add --transport http aidatatools-dev-data-quality-gate https://www.aidatatools.dev/api/mcp_server
```

### Codex

```toml
[mcp_servers.aidatatools-dev-data-quality-gate]
url = "https://www.aidatatools.dev/api/mcp_server"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "aidatatools-dev-data-quality-gate": {
      "type": "remote",
      "url": "https://www.aidatatools.dev/api/mcp_server",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add aidatatools-dev-data-quality-gate --url https://www.aidatatools.dev/api/mcp_server --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  aidatatools-dev-data-quality-gate:
    url: "https://www.aidatatools.dev/api/mcp_server"
```

### Other

```json
{
  "mcpServers": {
    "aidatatools-dev-data-quality-gate": {
      "type": "http",
      "url": "https://www.aidatatools.dev/api/mcp_server"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## 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.

### 2026-08-19 (score 67, +1)

No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-17 (score 66, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-15 (score 65, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-13 (score 64, +1)

No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-12 (score 63, −3)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “clean_scraped_data” rewrote its description, which is the text the model reads
- [security] Tool “clean_scraped_data_audited” rewrote its description, which is the text the model reads
- [functional regression] Schema quality: 268 → 441
- [functional regression] Schema quality: 268 → 395
- [functional] Schema quality: excellent → good
- [functional] New tool “clean_scraped_data”
- [functional] New tool “clean_scraped_data_audited”

### 2026-08-11 (score 66, +5)

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

### 2026-08-10 (score 61, 0)

- [functional improvement] Stability: unverified → 0.03

### 2026-08-09 (score 61)

First indexed and scored.

## MCP tools (3)

### `check_dataset_quality` (~195 tokens)

Call this before using any dataset. Returns a deterministic quality verdict (RELIABLE / USABLE_WITH_CLEANING / UNRELIABLE) with exact facts: completeness, nulls, type consistency, impossible values, duplicates, outliers, and (on financial/trading data) cross-source price divergence. 100% deterministic, no LLM. Free -- this MCP endpoint runs the engine directly; POST /api (plain REST, same engine) is x402-gated at $0.01/call instead. Input: rawJson (a JSON array of row objects, or a single object); datasetId is accepted but not resolvable on this deployment -- pass rawJson instead.

Input parameters:

- `datasetId` (string): An Apify dataset id. Not resolvable on this deployment; pass rawJson instead.
- `rawJson`: The dataset: a JSON array of row objects, or a single object.

### `clean_scraped_data` (~518 tokens)

PAID TIER, CALLED FROM A FREE ENDPOINT -- READ THIS FIRST: this tool does NOT return your cleaned data, and its response has NO `data` key. It returns {"status": "payment_required", "what_we_found_in_your_data": {...}, "get_the_repaired_data": {...}}. Do not treat its output as cleaned data and do not write it back into your dataset.
WHAT YOU GET HERE, FREE: an exact inventory of the defects in YOUR data -- which repair rules would fire, how many values each would change, and which cases cannot be repaired safely and need your decision.
HOW TO GET THE REPAIRED DATA: make the one call named in `get_the_repaired_data` -- POST https://www.aidatatools.dev/api/clean, $0.04 via x402, no account, no API key, no signup. That response body IS the cleaned dataset, in the shape you posted it.
WHY THE SPLIT: detection is free on this endpoint and always has been (check_dataset_quality reports the same defects). The repaired artifact is the paid product, because it is re-bought on every extraction run rather than cached like a verdict.
WHAT THE PAID CALL DOES: removes leftover HTML tags and entities, decodes mojibake ('Café' -> 'Café'), strips invisible characters (zero-width, BOM, soft hyphen), normalises non-breaking spaces and trims values -- across nested objects and arrays too. 100% deterministic, no LLM: the same input always yields byte-identical output, and cleaning twice equals cleaning once. It repairs how data was ENCODED, never what it SAYS: masked placeholders ('N/A', 'None'), near-duplicate rows and failed extractions ('access denied', 'captcha', which mean that record must be re-scraped) are reported with a proposal, never silently deleted or rewritten. The full boundary -- 7 rules applied automatically, 5 needing an explicit opt-in, 8 only ever reported -- is at GET https://www.aidatatools.dev/api/clean.

Input parameters:

- `options` (object): All optional. Every default is the safe one: with no options, the row count, every value's type, and the schema are all guaranteed unchanged.
- `rawJson` (required): The scraper output: a JSON array of row objects, a single object, or a CSV/plain-text string. The format is detected and the output mirrors the shape you sent.

### `clean_scraped_data_audited` (~420 tokens)

PAID TIER, CALLED FROM A FREE ENDPOINT -- READ THIS FIRST: this tool does NOT return your cleaned data, and its response has NO `data` key. It returns {"status": "payment_required", "what_we_found_in_your_data": {...}, "get_the_repaired_data": {...}}. Do not treat its output as cleaned data and do not write it back into your dataset.
WHAT YOU GET HERE, FREE: an exact inventory of the defects in YOUR data -- which repair rules would fire, how many values each would change, and which cases cannot be repaired safely and need your decision.
HOW TO GET THE REPAIRED DATA: make the one call named in `get_the_repaired_data` -- POST https://www.aidatatools.dev/api/clean/audit, $0.12 via x402, no account, no API key, no signup. That response body IS the cleaned dataset, in the shape you posted it.
WHY THE SPLIT: detection is free on this endpoint and always has been (check_dataset_quality reports the same defects). The repaired artifact is the paid product, because it is re-bought on every extraction run rather than cached like a verdict.
WHAT THE PAID CALL DOES: the same repair as clean_scraped_data, plus a complete audit trail: every transformation with its path, rule, before and after value, a replay_id, and input/output SHA-256. The ledger is a full inverse patch -- applying it in reverse reconstructs your original input byte for byte. Use it when you must be able to PROVE later what changed and why.

Input parameters:

- `options` (object): All optional. Every default is the safe one: with no options, the row count, every value's type, and the schema are all guaranteed unchanged.
- `rawJson` (required): The scraper output: a JSON array of row objects, a single object, or a CSV/plain-text string. The format is detected and the output mirrors the shape you sent.

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/aidatatools-dev-data-quality-gate/api-mcp-server#diagnostics

## Score history

- 2026-08-20: 67
- 2026-08-19: 67
- 2026-08-18: 66
- 2026-08-17: 66
- 2026-08-16: 65
- 2026-08-15: 65
- 2026-08-14: 64
- 2026-08-13: 64
- 2026-08-12: 63
- 2026-08-11: 66
- 2026-08-10: 61
- 2026-08-09: 61

## Links

- Remote endpoint: https://www.aidatatools.dev/api/mcp_server
- Website: https://www.aidatatools.dev/llms.txt
- Changelog RSS feed: https://verifymcp.io/servers/aidatatools-dev-data-quality-gate/api-mcp-server.xml
- Changelog JSON feed: https://verifymcp.io/servers/aidatatools-dev-data-quality-gate/api-mcp-server.json
- HTML version of this page: https://verifymcp.io/servers/aidatatools-dev-data-quality-gate/api-mcp-server