NLP Tools - Sentiment, NER, Toxicity & Language Detection
REMOTE · APIM-AI-APIS.AZURE-API.NET · SCANNED SEP 20
Toxicity, sentiment, NER, PII detection, and language identification tools
Available components
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. How we score → Why this is hard to score →
Endpoint Security77
- The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
- 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. See how to fix → View diagnostics → Fail
- HTTPS enforcement could not be verified: the plaintext port answered with HTTP 404, which proves neither a plaintext path nor enforcement. View diagnostics → Unverified
- The HSTS (Strict-Transport-Security) header is present. View diagnostics → Pass
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability66
- AI-judged instruction clarity (excellent).Pass
- 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. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 6 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 7 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the NLP Tools - Sentiment, NER, Toxicity & Language Detection MCP server?
NLP Tools - Sentiment, NER, Toxicity & Language Detection is a hosted endpoint at https://apim-ai-apis.azure-api.net/mcp/nlp/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
remote · apim-ai-apis.azure-api.net
claude mcp add --transport http fasuizu-br-nlp-tools 'https://apim-ai-apis.azure-api.net/mcp/nlp/mcp'
{
"mcpServers": {
"fasuizu-br-nlp-tools": {
"url": "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"
}
}
} {
"servers": {
"fasuizu-br-nlp-tools": {
"type": "http",
"url": "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"
}
}
} [mcp_servers.fasuizu-br-nlp-tools] url = "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"
{
"$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 mcp add fasuizu-br-nlp-tools --url 'https://apim-ai-apis.azure-api.net/mcp/nlp/mcp' --transport streamable-http
mcp_servers:
fasuizu-br-nlp-tools:
url: "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp" {
"McpServers": {
"fasuizu-br-nlp-tools": {
"Transport": "http",
"Url": "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"
}
}
} assistant mcp add fasuizu-br-nlp-tools -t streamable-http -u 'https://apim-ai-apis.azure-api.net/mcp/nlp/mcp'
{
"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.
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 20 Sept 26 0
- This server's schema is too large to store in full, so we cannot compare its tools day to day functional
- 19 Sept 26 0
- This server's schema is too large to store in full, so we cannot compare its tools day to day functional
- 18 Sept 26 +13
- Authorization: unverified → fail ▼ security
- HSTS header: unverified → pass ▲ security
- This server's schema is too large to store in full, so we cannot compare its tools day to day functional
- 17 Sept 26 −13
- Authorization: fail → unverified ▼ security
- HSTS header: pass → unverified ▼ security
- This server's schema is too large to store in full, so we cannot compare its tools day to day functional
- 16 Sept 26 0
- This server's schema is too large to store in full, so we cannot compare its tools day to day functional
- 15 Sept 26 0
- This server's schema is too large to store in full, so we cannot compare its tools day to day functional
- 14 Sept 26 0
- This server's schema is too large to store in full, so we cannot compare its tools day to day functional
- 13 Sept 26 0
- This server's schema is too large to store in full, so we cannot compare its tools day to day functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 20 Sept 2026 · Probed https://apim-ai-apis.azure-api.net/mcp/nlp/mcp
TLS valid
Negotiated TLS 1.3 with TLS_AES_256_GCM_SHA384 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=*.azure-api.net,O=Microsoft Corporation,L=Redmond,ST=WA,C=US | CN=Microsoft TLS G2 RSA CA OCSP 02,O=Microsoft Corporation,C=US | 29 Aug 2026 | 25 Feb 2027 | RSA 2048 | SHA384-RSA | 4100d61bf8ff53c0b65c03b120000000d61bf8 |
| SANs: *.azure-api.net, *.portal.azure-api.net, *.management.azure-api.net, *.scm.azure-api.net, *.configuration.azure-api.net, *.regional.azure-api.net, *.developer.azure-api.net, *.data.azure-api.net, *.portal-editor.azure-api.net, *.unique.azure-api.net, *.unique.portal.azure-api.net, *.unique.management.azure-api.net and 6 more | ||||||
| CN=Microsoft TLS G2 RSA CA OCSP 02,O=Microsoft Corporation,C=US (CA) | CN=Microsoft TLS RSA Root G2,O=Microsoft Corporation,C=US | 1 Aug 2025 | 3 Jun 2029 | RSA 4096 | SHA384-RSA | 330000000c4964a16f44203b2200000000000c |
| CN=Microsoft TLS RSA Root G2,O=Microsoft Corporation,C=US (CA) | CN=DigiCert Global Root G2,OU=www.digicert.com,O=DigiCert Inc,C=US | 21 May 2025 | 19 Jun 2029 | RSA 4096 | SHA384-RSA | b0c6b2c466917b04773c647d4afc0c8 |
Background: What to check on a remote MCP endpoint →
DNSSEC insecure
Validation of apim-ai-apis.azure-api.net. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | trust_anchor | 20326, 38696 | 8, 8 | Verified |
| net. | present | 37331 | 13 | Verified |
| azure-api.net. | absent | Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation |
Authentication Challenged, unverified
The endpoint asked for a token, but we could not retrieve and validate the RFC 9728 metadata that tells a client how to obtain one.
| Result | Challenged, unverified |
|---|---|
| Enforced | On tool calls |
| HTTP status | 200 |
| Header | Value |
|---|---|
| strict-transport-security | max-age=31536000; includeSubDomains |
| x-content-type-options | nosniff |
| x-frame-options | DENY |
| referrer-policy | strict-origin-when-cross-origin |
| permissions-policy | camera=(), microphone=(), geolocation=() |
Protected resource metadata
| Retrieved | No |
|---|---|
| Problem | no_resource_metadata |
Background: How OAuth 2.1 works in the 2026 MCP spec →
Transports 2 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| streamable-http | https://apim-ai-apis.azure-api.net/mcp/nlp/mcp | Verified | 200 | |
| http (plaintext) | http://apim-ai-apis.azure-api.net/mcp/nlp/mcp | Inconclusive | 404 |
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
analyze_sentiment Analyze Sentiment ~163
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)
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | – | Model variant: 'general' (default), 'financial', 'twitter' |
| text | string | yes | Text to analyze for sentiment (positive/negative) |
No output schema declared.
No examples provided.
analyze_toxicity Analyze Toxicity ~212
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
| Name | Type | Req | Description |
|---|---|---|---|
| text | string | yes | Text to analyze for toxicity (hate speech, insults, threats) |
No output schema declared.
No examples provided.
check_nlp_service Check NLP Service ~62
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
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
detect_language Detect Language ~174
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
| Name | Type | Req | Description |
|---|---|---|---|
| text | string | yes | Text to identify the language of |
| top_k | integer | – | Number of top language predictions to return |
No output schema declared.
No examples provided.
detect_pii Detect PII ~277
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
| Name | Type | Req | Description |
|---|---|---|---|
| redact | boolean | – | If true, return redacted text with PII replaced by [TYPE] |
| text | string | yes | Text to scan for personally identifiable information |
No output schema declared.
No examples provided.
extract_entities Extract Entities ~172
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
| Name | Type | Req | Description |
|---|---|---|---|
| text | string | yes | Text to extract named entities from (persons, organizations, locations) |
No output schema declared.
No examples provided.
What is the NLP Tools - Sentiment, NER, Toxicity & Language Detection MCP server?
NLP Tools - Sentiment, NER, Toxicity & Language Detection is an MCP server listed in the public MCP registry as io.github.fasuizu-br/nlp-tools. Toxicity, sentiment, NER, PII detection, and language identification tools. This page covers its hosted endpoint (https://apim-ai-apis.azure-api.net/mcp/nlp/mcp).
Is the NLP Tools - Sentiment, NER, Toxicity & Language Detection MCP server safe to use?
NLP Tools - Sentiment, NER, Toxicity & Language Detection scores 85 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the NLP Tools - Sentiment, NER, Toxicity & Language Detection MCP server expose?
NLP Tools - Sentiment, NER, Toxicity & Language Detection exposes 6 tools: analyze_toxicity, analyze_sentiment, extract_entities, detect_pii, detect_language, check_nlp_service. Their descriptions and schemas cost roughly 1,060 tokens of context every time the server is loaded.
Does the NLP Tools - Sentiment, NER, Toxicity & Language Detection MCP server require authentication?
Yes. NLP Tools - Sentiment, NER, Toxicity & Language Detection asked us for credentials when we connected, so you will need to authorise it in your MCP client before it can do anything.
Is the NLP Tools - Sentiment, NER, Toxicity & Language Detection MCP server still maintained?
NLP Tools - Sentiment, NER, Toxicity & Language Detection is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.