mcp

Public MCP server for the LLM Search Engine

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What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    Public MCP server for the LLM Search Engine

    How to connect

    How to connect mcp to Claude

    The server address to paste into Claude settings:

    https://llmse.ai/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect mcp to ChatGPT

    The server address to paste into ChatGPT settings:

    https://llmse.ai/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect mcp to Cursor

    The server address to paste into Cursor settings:

    https://llmse.ai/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (10)

    Raw names from tools/list. Only developers need these.

    classify_urlClassify 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
    select_advertiserSelect 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)
    analyze_seoAnalyze 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
    analyze_eeatAnalyze 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
    analyze_aeoAnalyze 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
    analyze_wcagAnalyze 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
    analyze_garmCompute 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
    analyze_readabilityAnalyze 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
    auditPerform 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
    find_similar_sitesFind 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