# NLP Tools - Sentiment, NER, Toxicity & Language Detection (remote · apim-ai-apis.azure-api.net)

Toxicity, sentiment, NER, PII detection, and language identification tools

- Trust score: 73/100 (medium)
- Change this week: +7
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `apim-ai-apis.azure-api.net`: 73/100 (this document), [markdown](https://verifymcp.io/servers/fasuizu-br-nlp-tools/mcp-nlp-mcp.md), [page](https://verifymcp.io/servers/fasuizu-br-nlp-tools/mcp-nlp-mcp)

## Channel facts

- Endpoint: `https://apim-ai-apis.azure-api.net/mcp/nlp/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.1.0`

## 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 2026-08-03.

- **Endpoint Security**: 77/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation is enforced on tool calls, but the challenge carries no valid RFC 9728 metadata, so a client cannot discover where to get a token.
  - HTTPS not yet verified: we couldn't determine whether a plaintext access path exists.
  - 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**: 66/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 1175 tokens (~195/item across 6 items; 6 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 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 --transport http fasuizu-br-nlp-tools https://apim-ai-apis.azure-api.net/mcp/nlp/mcp
```

### Codex

```toml
[mcp_servers.fasuizu-br-nlp-tools]
url = "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "fasuizu-br-nlp-tools": {
      "type": "remote",
      "url": "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add fasuizu-br-nlp-tools --url https://apim-ai-apis.azure-api.net/mcp/nlp/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  fasuizu-br-nlp-tools:
    url: "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"
```

### Other

```json
{
  "mcpServers": {
    "fasuizu-br-nlp-tools": {
      "type": "http",
      "url": "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"
    }
  }
}
```

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.

### 2026-08-03 (score 73, +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.

### 2026-07-31 (score 72, +4)

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

### 2026-07-29 (score 68, +19)

- [security regression] Authorization: unverified → fail
- [security improvement] HSTS header: unverified → pass
- [security improvement] Transport: fail → pass

### 2026-07-28 (score 49, −17)

- [security regression] Authorization: fail → unverified
- [security regression] HSTS header: pass → unverified
- [security regression] Transport: pass → fail

### 2026-07-27 (score 66, 0)

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

### 2026-07-26 (score 66)

First indexed and scored.

## MCP tools (6)

### `analyze_toxicity` (~212 tokens)

Analyze Toxicity

Analyze text for toxic content.

Returns scores for 6 categories: toxic, severe_toxic, obscene, threat,
insult, identity_hate. Each score is 0.0-1.0.
BERT-based classifier with sub-15ms latency on GPU.

Args:
    text: Text to analyze for toxicity (hate speech, insults, threats).

Returns:
    dict with keys:
        - toxic (float 0-1): Overall toxicity score
        - severe_toxic (float 0-1): Severe toxicity score
        - obscene (float 0-1): Obscenity score
        - threat (float 0-1): Threat score
        - insult (float 0-1): Insult score
        - identity_hate (float 0-1): Identity-based hate score
        - is_toxic (bool): Whether text exceeds toxicity threshold

Input parameters:

- `text` (string, required): Text to analyze for toxicity (hate speech, insults, threats)

### `analyze_sentiment` (~163 tokens)

Analyze Sentiment

Analyze text sentiment.

Returns positive/negative classification with confidence scores.
DistilBERT-based with sub-10ms latency. Multiple domain-specific
model variants available.

Args:
    text: Text to analyze for sentiment (positive/negative).
    model: Model variant -- 'general' (default), 'financial', 'twitter'.

Returns:
    dict with keys:
        - label (str): 'positive' or 'negative'
        - score (float 0-1): Confidence score for the predicted label
        - scores (dict): All label scores (positive, negative)

Input parameters:

- `model` (string): Model variant: 'general' (default), 'financial', 'twitter'
- `text` (string, required): Text to analyze for sentiment (positive/negative)

### `extract_entities` (~172 tokens)

Extract Entities

Extract named entities (NER) from text.

Identifies persons, organizations, locations, and miscellaneous entities
with span offsets and confidence scores. BERT-NER based with sub-50ms latency.

Args:
    text: Text to extract named entities from.

Returns:
    dict with keys:
        - entities (list): Detected entities, each containing:
            - text (str): Entity text
            - label (str): Entity type (PER, ORG, LOC, MISC)
            - start (int): Character offset start
            - end (int): Character offset end
            - score (float 0-1): Confidence score
        - count (int): Total number of entities found

Input parameters:

- `text` (string, required): Text to extract named entities from (persons, organizations, locations)

### `detect_pii` (~277 tokens)

Detect PII

Detect personally identifiable information (PII) in text.

Finds emails, phone numbers, SSNs, credit cards, IP addresses, and
person names. Optionally returns redacted text with PII replaced by
type labels (e.g. [EMAIL], [PHONE]). BERT-NER + regex ensemble.

Args:
    text: Text to scan for personally identifiable information.
    redact: If true, return redacted text with PII replaced by [TYPE].

Returns:
    dict with keys:
        - pii_found (list): Detected PII items, each containing:
            - text (str): The PII value found
            - type (str): PII type (EMAIL, PHONE, SSN, CREDIT_CARD, IP, PERSON)
            - start (int): Character offset start
            - end (int): Character offset end
            - score (float 0-1): Detection confidence
        - count (int): Total PII items found
        - redacted_text (str|null): Text with PII replaced (when redact=true)
        - has_pii (bool): Whether any PII was detected

Input parameters:

- `redact` (boolean): If true, return redacted text with PII replaced by [TYPE]
- `text` (string, required): Text to scan for personally identifiable information

### `detect_language` (~174 tokens)

Detect Language

Detect the language of text.

Supports 176 languages using fastText. Sub-1ms inference latency.
Returns ISO 639-1 codes with confidence scores.

Args:
    text: Text to identify the language of.
    top_k: Number of top language predictions to return (default: 3).

Returns:
    dict with keys:
        - language (str): Top predicted language ISO 639-1 code
        - confidence (float 0-1): Confidence for top prediction
        - predictions (list): Top-k predictions, each with:
            - language (str): ISO 639-1 code
            - confidence (float 0-1): Prediction confidence

Input parameters:

- `text` (string, required): Text to identify the language of
- `top_k` (integer): Number of top language predictions to return

### `check_nlp_service` (~62 tokens)

Check NLP Service

Check health status of NLP API services and loaded models.

Returns:
    dict with keys:
        - status (str): 'healthy' or error state
        - models (dict): Loaded model status per capability
        - version (str): API version

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/fasuizu-br-nlp-tools/mcp-nlp-mcp#diagnostics

## Score history

- 2026-08-03: 73
- 2026-08-02: 72
- 2026-08-01: 72
- 2026-07-31: 72
- 2026-07-30: 68
- 2026-07-29: 68
- 2026-07-28: 49
- 2026-07-27: 66
- 2026-07-26: 66

## Links

- Remote endpoint: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp
- Repository: https://github.com/fasuizu-br/speech-ai-examples
- Website: https://brainiall.com/
- Changelog RSS feed: https://verifymcp.io/servers/fasuizu-br-nlp-tools/mcp-nlp-mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/fasuizu-br-nlp-tools/mcp-nlp-mcp/changelog.json
- HTML version of this page: https://verifymcp.io/servers/fasuizu-br-nlp-tools/mcp-nlp-mcp