# ai.llmse/mcp (remote · llmse.ai)

Public MCP server for the LLM Search Engine

- Trust score: 63/100 (medium)
- Change this week: +7
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-04

## Components

- remote · `llmse.ai`: 63/100 (this document), [markdown](https://verifymcp.io/servers/ai-llmse-mcp/llmse.md), [page](https://verifymcp.io/servers/ai-llmse-mcp/llmse)

## Channel facts

- Endpoint: `https://llmse.ai/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.3.12`

## 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 2026-08-04.

- **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 10 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**: 54/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 2823 tokens (~282/item across 10 items; 10 tools + 0 resources), over budget; trim descriptions and params.
  - 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**: 71/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 0% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **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 ai-llmse-mcp https://llmse.ai/mcp
```

### Codex

```toml
[mcp_servers.ai-llmse-mcp]
url = "https://llmse.ai/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ai-llmse-mcp": {
      "type": "remote",
      "url": "https://llmse.ai/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add ai-llmse-mcp --url https://llmse.ai/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  ai-llmse-mcp:
    url: "https://llmse.ai/mcp"
```

### Other

```json
{
  "mcpServers": {
    "ai-llmse-mcp": {
      "type": "http",
      "url": "https://llmse.ai/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.

### 2026-08-04 (score 63, +1)

No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-02 (score 62, +1)

No change was recorded against any check on this day. Stability & Change Management went from 20 to 23. That category is still filling its 30-day observation window: 6 days of observed history at the previous scan, 7 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 61, +3)

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

### 2026-07-30 (score 58, +1)

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

### 2026-07-29 (score 57, +1)

No change was recorded against any check on this day. Stability & Change Management went from 7 to 10. That category is still filling its 30-day observation window: 2 days of observed history at the previous scan, 3 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-27 (score 56, +1)

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

### 2026-07-26 (score 55)

First indexed and scored.

## MCP tools (10)

### `classify_url` (~181 tokens)

Classify a website URL into category, subcategory, language, and sentiment.

    Fetches the URL content and uses AI for classification.
    Results are cached for fast subsequent lookups.
    Rate limited to 1 request per minute per domain.

    Args:
        url: The website URL to classify (e.g., "https://example.com").

    Returns:
        Classification result with:
        - url: The normalized URL
        - category: Main category (e.g., "Sports", "Technology")
        - subcategory: Specific subcategory
        - language: Detected content language
        - sentiment: Content sentiment (Good/Neutral/Bad)
        - age: Target age group (if available)
        - gender: Target gender (if available)
        - cached: Whether result was from cache

Input parameters:

- `url` (string, required)

### `select_advertiser` (~377 tokens)

Select the best advertisers based on website demographics.

    Matches advertisers to website content based on classification demographics.
    Provide either a URL (classification will be fetched) or demographics directly.
    Rate limited to 1 request per minute per domain when using URL.

    Scoring weights:
    - Category match: +10 points
    - Age match: +5 points
    - Gender match: +3 points
    - Sentiment match: +2 points
    - Higher CPM bid as tiebreaker

    Args:
        url: URL to match advertisers for (fetches classification from cache).
        category: Target category (e.g., "Sports", "Automotive").
        subcategory: Target subcategory.
        age: Target age group (e.g., "18-24", "25-34", "31-51").
        gender: Target gender ("male", "female", or "all").
        sentiment: Content sentiment ("Good", "Neutral", or "Bad").
        limit: Number of advertisers to return (1-10, default 3).
        min_cpm: Minimum CPM cost filter (e.g., 5.0 for $5+ CPM).
        max_cpm: Maximum CPM cost filter (e.g., 10.0 for $10 or less CPM).

    Returns:
        Dictionary with:
        - matches: List of matched advertisers with scores
        - match_count: Number of matches found
        - classification: URL classification (if URL provided)
        - demographics: Provided demographics (if no URL)

Input parameters:

- `age`
- `category`
- `gender`
- `limit` (integer)
- `max_cpm`
- `min_cpm`
- `sentiment`
- `subcategory`
- `url`

### `analyze_seo` (~175 tokens)

Analyze a website URL for SEO optimizations.

    Fetches the URL content and analyzes HTML for possible SEO improvements.
    Results are cached for fast subsequent lookups.
    Rate limited to 1 request per minute per domain.

    Args:
        url: The website URL to analyze (e.g., "https://example.com").

    Returns:
        SEO analysis result with:
        - url: The analyzed URL
        - score: Overall SEO score (0-100)
        - grade: Letter grade (A-F)
        - issues: List of SEO issues found (critical, warnings, info)
        - meta: Extracted meta information (title, description, headings, etc.)
        - recommendations: Prioritized list of improvements
        - cached: Whether result was from cache

Input parameters:

- `url` (string, required)

### `analyze_eeat` (~314 tokens)

Analyze a website URL for E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness).

    Evaluates content quality signals based on Google's Search Quality Rater Guidelines
    and "Creating helpful content" documentation. Detects EEAT signals including:
    - Experience: First-person language, case studies, testimonials, years of experience
    - Expertise: Author credentials, certifications, professional memberships, topic depth
    - Authoritativeness: Organization schema, awards, trust badges, media mentions
    - Trustworthiness: HTTPS, contact info, privacy policy, source citations

    Also detects YMYL (Your Money or Your Life) content for health, financial, and legal topics.

    Results are cached for fast subsequent lookups.
    Rate limited to 1 request per minute per domain.

    Args:
        url: The website URL to analyze (e.g., "https://example.com").

    Returns:
        EEAT analysis result with:
        - url: The analyzed URL
        - score: Overall EEAT score (0-100)
        - grade: Letter grade (A-F)
        - scores: Individual category scores (experience, expertise, authoritativeness, trustworthiness)
        - issues: Categorized issues (critical, warnings, info)
        - signals: Detected EEAT signals
        - meta: Extracted meta information
        - recommendations: Prioritized list of improvements
        - cached: Whether result was from cache

Input parameters:

- `url` (string, required)

### `analyze_aeo` (~383 tokens)

Analyze how well content is optimized for AI answer engines.

    Evaluates content for AI answer engines (ChatGPT, Perplexity, Gemini, Claude).
    Combines Q&A pattern detection, snippet extractability, and entity clarity
    analysis with a full Citation Readiness assessment.

    AEO Scoring Framework (100 points):
    - Answer Format Detection: 30 points (Q&A extractability patterns)
    - FAQ Schema Presence: 20 points (FAQPage schema markup)
    - HowTo Schema Presence: 15 points (HowTo schema markup)
    - Direct Answer Snippets: 20 points (short extractable blocks <50 words)
    - Entity Clarity Score: 15 points (clear entity definitions)

    Neutral Schema Scoring: If no FAQ/HowTo-style content detected, those
    schema metrics score full points rather than penalizing.

    Grade Scale: A (85-100), B (70-84), C (55-69), D (40-54), F (0-39)

    Results are cached for fast subsequent lookups.
    Rate limited to 1 request per minute per domain.

    Args:
        url: The website URL to analyze (e.g., "https://example.com").

    Returns:
        AEO analysis with:
        - url: The analyzed URL
        - aeo_score: Overall AEO score (0-100)
        - aeo_grade: Letter grade (A-F)
        - aeo_metrics: Individual metric scores
        - citation: Full Citation Readiness analysis (score, grade, issues, signals)
        - issues: Problems detected (critical, warnings, info)
        - signals: Positive signals detected
        - recommendations: Prioritized improvements
        - cached: Whether result was from cache

Input parameters:

- `url` (string, required)

### `analyze_wcag` (~254 tokens)

Analyze a website URL for WCAG 2.1 Level A accessibility issues.

    Automated static HTML analysis covering approximately 30-40% of WCAG 2.1
    Level A criteria. Checks include: image alt text, form labels, heading
    hierarchy, page title, html lang, empty links/buttons, ARIA labels,
    duplicate IDs, skip navigation, table headers, landmarks, viewport zoom,
    autoplay media, and tabindex ordering.

    Manual testing is required for full WCAG compliance assessment.

    Results are cached for fast subsequent lookups.
    Rate limited to 1 request per minute per domain.

    Args:
        url: The website URL to analyze (e.g., "https://example.com").

    Returns:
        WCAG analysis with:
        - url: The analyzed URL
        - score: Accessibility score (0-100)
        - grade: Letter grade (A-F)
        - issues: Categorized issues (critical, warnings, info)
        - meta: Extracted accessibility metadata
        - recommendations: Prioritized improvements
        - coverage_note: Disclaimer about automated coverage
        - cached: Whether result was from cache

Input parameters:

- `url` (string, required)

### `analyze_garm` (~302 tokens)

Compute GARM brand safety score for a website or category.

    Based on the GARM (Global Alliance for Responsible Media) Brand Suitability
    Framework. Maps content categories to 11 GARM sensitive content categories
    with risk levels (Floor, High, Medium, Low).

    Can either:
    1. Provide a URL - classification will be fetched and mapped to GARM
    2. Provide category and sentiment directly for instant scoring

    Score interpretation: higher = safer for advertising.
    Floor categories (e.g., Adult) always score 0/F regardless of sentiment.

    Args:
        category: LLMSE category (e.g., "Adult", "Politics", "Sports").
        sentiment: Content sentiment ("Bad", "Neutral", "Good").
        url: Optional URL to analyze (fetches classification from cache).

    Returns:
        GARM brand safety analysis with:
        - score: Brand safety score (0-100, higher = safer)
        - grade: Letter grade (A-F)
        - garm_category: Matched GARM category name or None
        - risk_level: "floor"|"high"|"medium"|"low"|"none"
        - is_floor: True if not suitable for any advertising
        - issues: Categorized issues {critical, warnings, info}
        - recommendations: Improvement suggestions

Input parameters:

- `category`
- `sentiment`
- `url`

### `analyze_readability` (~316 tokens)

Analyze a website URL for content readability using Flesch Reading Ease.

    Extracts plain text from HTML and computes readability metrics including
    Flesch Reading Ease score, Flesch-Kincaid grade level, reading time,
    and word/sentence statistics.

    Grade Scale (web-optimized):
    - A (60-100): Easy, 6th-8th grade — ideal for web content
    - B (50-59): Fairly easy, some high school
    - C (30-49): Standard, college level
    - D (10-29): Difficult, graduate level
    - F (0-9): Very difficult, professional/academic

    Results are cached for fast subsequent lookups.
    Rate limited to 1 request per minute per domain.

    Args:
        url: The website URL to analyze (e.g., "https://example.com").

    Returns:
        Readability analysis with:
        - url: The analyzed URL
        - score: Flesch Reading Ease score (0-100, higher = easier)
        - grade: Letter grade (A-F)
        - flesch_kincaid_grade_level: US school grade level equivalent
        - reading_time_minutes: Estimated reading time in minutes
        - word_count: Total word count
        - sentence_count: Total sentence count
        - difficult_words: Count of difficult/uncommon words
        - cached: Whether result was from cache

Input parameters:

- `url` (string, required)

### `audit` (~334 tokens)

Perform comprehensive audit of a website URL.

    Fetches the URL content ONCE and provides a combined report with:
    - Classification: category, subcategory, language, sentiment, demographics
    - SEO Analysis: score, grade, issues, recommendations
    - EEAT Analysis: experience, expertise, authoritativeness, trustworthiness scores
    - AEO Analysis: AI answer engine optimization score, metrics, issues, signals
      (includes full Citation Readiness analysis in the nested 'citation' key)
    - Advertiser Matching: best-fit advertising networks with scores
    - Similar Sites: competitor/related sites from the same category

    This is more efficient than calling classify_url, analyze_seo, analyze_eeat,
    analyze_aeo, select_advertiser, and find_similar_sites separately as it only
    fetches the page once.

    Args:
        url: The website URL to audit (e.g., "https://example.com").

    Returns:
        Comprehensive audit report with:
        - url: The analyzed URL
        - classification: Category, subcategory, language, sentiment, demographics
        - seo: Score, grade, issues, recommendations
        - eeat: EEAT score, grade, category scores, issues, signals
        - aeo: AEO score, grade, metrics, issues, signals (includes citation results)
        - advertisers: Matched advertising networks with scores
        - similar_sites: Related sites from the same category (up to 10)
        - cached: Whether result was from cache

Input parameters:

- `url` (string, required)

### `find_similar_sites` (~187 tokens)

Find similar or competitor websites based on classification.

    Takes a URL, classifies it (or uses cached classification), and returns
    other websites from the same category and subcategory. Useful for
    competitive analysis and discovering related content.
    Rate limited to 1 request per minute per domain.

    Args:
        url: The website URL to find similar sites for.
        limit: Maximum number of similar sites to return (1-50, default 10).

    Returns:
        Dictionary with:
        - url: The input URL (normalized)
        - classification: The URL's category and subcategory
        - similar_sites: List of similar URLs from the same category
        - total_in_category: Total sites in this category/subcategory
        - cached: Whether the classification was from cache

Input parameters:

- `limit` (integer)
- `url` (string, required)

## Diagnostics

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

## Score history

- 2026-08-04: 63
- 2026-08-03: 62
- 2026-08-02: 62
- 2026-08-01: 61
- 2026-07-31: 61
- 2026-07-30: 58
- 2026-07-29: 57
- 2026-07-28: 56
- 2026-07-27: 56
- 2026-07-26: 55

## Links

- Remote endpoint: https://llmse.ai/mcp
- Changelog RSS feed: https://verifymcp.io/servers/ai-llmse-mcp/llmse/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/ai-llmse-mcp/llmse/changelog.json
- HTML version of this page: https://verifymcp.io/servers/ai-llmse-mcp/llmse