diff --git a/integrations/mcp-memgraph/pyproject.toml b/integrations/mcp-memgraph/pyproject.toml
index 3f454f9..a225025 100644
--- a/integrations/mcp-memgraph/pyproject.toml
+++ b/integrations/mcp-memgraph/pyproject.toml
@@ -27,6 +27,7 @@ dependencies = [
   "memgraph-toolbox>=0.1.7",
   "pytest>=8.3.5",
   "fastmcp>=2.13.0.2",
+  "sentence-transformers>=2.2.0",
 ]
 
 [project.optional-dependencies]
diff --git a/integrations/mcp-memgraph/src/mcp_memgraph/servers/__init__.py b/integrations/mcp-memgraph/src/mcp_memgraph/servers/__init__.py
index 002c6c8..7b11c27 100644
--- a/integrations/mcp-memgraph/src/mcp_memgraph/servers/__init__.py
+++ b/integrations/mcp-memgraph/src/mcp_memgraph/servers/__init__.py
@@ -23,6 +23,14 @@
             "support for autonomous index management"
         ),
     },
+    "sic-classification": {
+        "module": "mcp_memgraph.servers.sic_classification",
+        "emoji": "🏭",
+        "description": (
+            "SIC Classification server for looking up Standard Industrial "
+            "Classification codes using vector search and AI"
+        ),
+    },
     # Future servers can be added here:
     # "hygm": {
     #     "module": "mcp_memgraph.servers.hygm",
diff --git a/integrations/mcp-memgraph/src/mcp_memgraph/servers/sic_classification.py b/integrations/mcp-memgraph/src/mcp_memgraph/servers/sic_classification.py
new file mode 100644
index 0000000..9baec79
--- /dev/null
+++ b/integrations/mcp-memgraph/src/mcp_memgraph/servers/sic_classification.py
@@ -0,0 +1,644 @@
+"""SIC Classification MCP Server.
+
+This server provides SIC (Standard Industrial Classification) code lookup
+capabilities using vector search in Memgraph.
+
+The server uses:
+- Vector search to find relevant IndustryGroup nodes based on user description
+- Node neighborhood exploration to gather context (Industries, MajorGroups)
+- LLM sampling to determine the best matching SIC code
+"""
+
+from fastmcp import FastMCP, Context
+from memgraph_toolbox.api.memgraph import Memgraph
+from memgraph_toolbox.tools.node_vector_search import NodeVectorSearchTool
+from memgraph_toolbox.tools.node_neighborhood import NodeNeighborhoodTool
+from memgraph_toolbox.utils.logger import logger_init
+
+from typing import Any, Dict, List
+import json
+
+from mcp_memgraph.config import get_memgraph_config, get_mcp_config
+
+# Get configuration instances
+memgraph_config = get_memgraph_config()
+mcp_config = get_mcp_config()
+
+# Configure logging
+logger = logger_init("mcp-memgraph-sic")
+
+# Initialize FastMCP server
+mcp = FastMCP("mcp-memgraph-sic")
+
+# Initialize Memgraph client
+logger.info(
+    "Connecting to Memgraph db '%s' at %s with user '%s'",
+    memgraph_config.database,
+    memgraph_config.url,
+    memgraph_config.username,
+)
+
+db = Memgraph(**memgraph_config.get_client_config())
+
+# Vector index configuration
+SIC_VECTOR_INDEX = "sic_industry_group_embedding"
+EMBEDDING_DIMENSION = 384
+EMBEDDING_MODEL = "all-MiniLM-L6-v2"
+
+# Lazy-loaded embedding model
+_embedding_model = None
+
+
+def get_embedding_model():
+    """Get or initialize the sentence transformer model (lazy loading)."""
+    global _embedding_model
+    if _embedding_model is None:
+        try:
+            from sentence_transformers import SentenceTransformer
+
+            logger.info("Loading embedding model: %s", EMBEDDING_MODEL)
+            _embedding_model = SentenceTransformer(EMBEDDING_MODEL)
+            logger.info("Embedding model loaded successfully")
+        except ImportError:
+            logger.error(
+                "sentence_transformers not installed. "
+                "Install with: pip install sentence-transformers"
+            )
+            raise
+    return _embedding_model
+
+
+def get_embedding_from_text(text: str) -> List[float]:
+    """
+    Generate an embedding vector for the given text using sentence transformers.
+
+    Args:
+        text: The text to embed
+
+    Returns:
+        List of floats representing the embedding vector
+    """
+    model = get_embedding_model()
+    embedding = model.encode(text, convert_to_numpy=True)
+    return embedding.tolist()
+
+
+def get_node_context(node_id: int, max_distance: int = 1) -> List[Dict[str, Any]]:
+    """
+    Get the neighborhood context around a node.
+
+    Args:
+        node_id: The ID of the node
+        max_distance: Maximum hops to traverse
+
+    Returns:
+        List of neighboring node properties
+    """
+    try:
+        results = NodeNeighborhoodTool(db=db).call(
+            {"node_id": str(node_id), "max_distance": max_distance, "limit": 50}
+        )
+        return results
+    except Exception as e:
+        logger.error("Failed to get node neighborhood: %s", str(e))
+        return []
+
+
+def perform_vector_search(
+    query_vector: List[float], limit: int = 3
+) -> List[Dict[str, Any]]:
+    """
+    Perform vector search on the SIC index.
+
+    Args:
+        query_vector: The embedding vector to search with
+        limit: Number of results to return
+
+    Returns:
+        List of matching nodes with their properties and distances
+    """
+    try:
+        results = NodeVectorSearchTool(db=db).call(
+            {
+                "index_name": SIC_VECTOR_INDEX,
+                "query_vector": query_vector,
+                "limit": limit,
+            }
+        )
+        return results
+    except Exception as e:
+        logger.error("Vector search failed: %s", str(e))
+        return []
+
+
+def get_node_id_by_code(code: str, label: str = "IndustryGroup") -> int | None:
+    """
+    Get the internal node ID by the SIC code.
+
+    Args:
+        code: The SIC code
+        label: The node label
+
+    Returns:
+        The node ID or None if not found
+    """
+    try:
+        query = f"MATCH (n:{label} {{code: $code}}) RETURN id(n) as node_id"
+        results = db.query(query, {"code": code})
+        if results:
+            return results[0]["node_id"]
+        return None
+    except Exception as e:
+        logger.error("Failed to get node ID: %s", str(e))
+        return None
+
+
+async def generate_clarifying_facts(
+    prompt: str,
+    candidates: List[Dict[str, Any]],
+    ctx: Context,
+) -> Dict[str, Any]:
+    """
+    Generate 3 clarifying facts for the user to choose from when confidence is low.
+
+    Args:
+        prompt: The user's business description
+        candidates: List of candidate SIC entries with context
+        ctx: FastMCP context for sampling
+
+    Returns:
+        Dictionary with facts and initial analysis
+    """
+    # Format candidates for the LLM
+    candidates_text = _format_candidates_for_prompt(candidates)
+
+    analysis_prompt = f"""You are an expert in SIC (Standard Industrial Classification) codes.
+
+A user has described their business activity as follows:
+"{prompt}"
+
+Based on vector similarity search, here are the top candidate SIC classifications:
+{candidates_text}
+
+The description is ambiguous or could match multiple SIC codes. Generate exactly 3 clarifying statements that would help distinguish between the possible classifications.
+
+Each fact should be a simple statement that the user can confirm or deny about their business.
+
+Return your response as JSON with this structure:
+{{
+    "fact_1": "Your business primarily involves [specific activity A]",
+    "fact_2": "Your business primarily involves [specific activity B]",
+    "fact_3": "Your business primarily involves [specific activity C]",
+    "reasoning": "Brief explanation of why these facts help distinguish between candidates"
+}}
+
+IMPORTANT:
+- Each fact should clearly map to one of the candidate SIC codes
+- Facts should be mutually exclusive where possible
+- Use simple, clear language the user can easily understand
+"""
+
+    try:
+        response = await ctx.sample(
+            messages=analysis_prompt,
+            system_prompt=(
+                "You are a SIC classification expert. Generate clarifying facts "
+                "to help distinguish between possible SIC codes. "
+                "Return only valid JSON, no additional text or markdown."
+            ),
+            temperature=0.3,
+            max_tokens=500,
+        )
+
+        response_text = _extract_response_text(response)
+        result = json.loads(response_text)
+        return result
+
+    except json.JSONDecodeError as e:
+        logger.error("Failed to parse clarifying facts response: %s", str(e))
+        return {"error": "Failed to generate clarifying facts"}
+    except Exception as e:
+        logger.error("Clarifying facts generation failed: %s", str(e))
+        return {"error": f"Failed to generate facts: {str(e)}"}
+
+
+async def analyze_with_user_selection(
+    prompt: str,
+    candidates: List[Dict[str, Any]],
+    selected_fact: str,
+    ctx: Context,
+) -> Dict[str, Any]:
+    """
+    Final analysis after user has selected a clarifying fact.
+
+    Args:
+        prompt: The user's business description
+        candidates: List of candidate SIC entries with context
+        selected_fact: The fact the user confirmed
+        ctx: FastMCP context for sampling
+
+    Returns:
+        Dictionary with final SIC code selection
+    """
+    candidates_text = _format_candidates_for_prompt(candidates)
+
+    analysis_prompt = f"""You are an expert in SIC (Standard Industrial Classification) codes.
+
+A user has described their business activity as follows:
+"{prompt}"
+
+The user has confirmed the following fact about their business:
+"{selected_fact}"
+
+Based on vector similarity search, here are the candidate SIC classifications:
+{candidates_text}
+
+Given the user's confirmation, select the BEST matching SIC code.
+
+Return your response as JSON with this structure:
+{{
+    "selected_code": "XXXX",
+    "selected_name": "Name of the selected classification",
+    "confidence": "high",
+    "explanation": "Detailed explanation of why this code was selected based on the confirmed fact"
+}}
+
+IMPORTANT:
+- The confidence should now be "high" since the user has clarified their business
+- Use the most specific code possible (4-digit Industry code if available)
+"""
+
+    try:
+        response = await ctx.sample(
+            messages=analysis_prompt,
+            system_prompt=(
+                "You are a SIC classification expert. Select the best SIC code "
+                "based on the user's confirmed business activity. "
+                "Return only valid JSON, no additional text or markdown."
+            ),
+            temperature=0.1,
+            max_tokens=500,
+        )
+
+        response_text = _extract_response_text(response)
+        result = json.loads(response_text)
+        return result
+
+    except json.JSONDecodeError as e:
+        logger.error("Failed to parse final analysis response: %s", str(e))
+        return {"error": "Failed to parse final classification"}
+    except Exception as e:
+        logger.error("Final analysis failed: %s", str(e))
+        return {"error": f"Final classification failed: {str(e)}"}
+
+
+def _format_candidates_for_prompt(candidates: List[Dict[str, Any]]) -> str:
+    """Format candidates list into text for LLM prompts."""
+    candidates_text = ""
+    for i, candidate in enumerate(candidates, 1):
+        candidates_text += f"\n--- Candidate {i} ---\n"
+        candidates_text += f"Industry Group Code: {candidate.get('code', 'N/A')}\n"
+        candidates_text += f"Industry Group Name: {candidate.get('name', 'N/A')}\n"
+        candidates_text += f"Context: {candidate.get('context_text', 'N/A')}\n"
+
+        if "related_industries" in candidate:
+            candidates_text += "Related Industries:\n"
+            for industry in candidate["related_industries"][:5]:
+                ind_code = industry.get("code", "")
+                ind_name = industry.get("name", "")
+                ind_desc = industry.get("description", "")
+                candidates_text += f"  - {ind_code}: {ind_name} - {ind_desc}\n"
+
+        if "major_group" in candidate:
+            mg = candidate["major_group"]
+            mg_code = mg.get("code", "")
+            mg_name = mg.get("name", "")
+            mg_desc = mg.get("description", "")
+            candidates_text += f"Major Group: {mg_code}: {mg_name} - {mg_desc}\n"
+
+    return candidates_text
+
+
+def _extract_response_text(response) -> str:
+    """Extract text from various response types."""
+    if isinstance(response, str):
+        response_text = response.strip()
+    elif hasattr(response, "text"):
+        response_text = response.text.strip()
+    else:
+        response_text = str(response).strip()
+
+    # Remove markdown code blocks if present
+    if response_text.startswith("```"):
+        lines = response_text.split("\n")
+        response_text = "\n".join(
+            line
+            for line in lines
+            if not line.strip().startswith("```") and line.strip().lower() != "json"
+        ).strip()
+
+    return response_text
+
+
+async def analyze_and_select_sic_code(
+    prompt: str,
+    candidates: List[Dict[str, Any]],
+    ctx: Context,
+) -> Dict[str, Any]:
+    """
+    Use LLM sampling to analyze candidates and select the best SIC code.
+    Returns confidence as either "high" or "low".
+
+    Args:
+        prompt: The user's business description
+        candidates: List of candidate SIC entries with context
+        ctx: FastMCP context for sampling
+
+    Returns:
+        Dictionary with selected SIC code and explanation
+    """
+    candidates_text = _format_candidates_for_prompt(candidates)
+
+    analysis_prompt = f"""You are an expert in SIC (Standard Industrial Classification) codes.
+
+A user has described their business activity as follows:
+"{prompt}"
+
+Based on vector similarity search, here are the top candidate SIC classifications:
+{candidates_text}
+
+Your task:
+1. Analyze how well each candidate matches the user's business description
+2. Select the BEST matching SIC code (4-digit code from Industries if specific match, or Industry Group code if more general)
+3. Determine if you are confident in this match
+
+Return your response as JSON with this structure:
+{{
+    "selected_code": "XXXX",
+    "selected_name": "Name of the selected classification",
+    "confidence": "high" or "low",
+    "explanation": "Detailed explanation of why this code was selected"
+}}
+
+CONFIDENCE RULES:
+- "high": The user's description clearly and unambiguously matches one SIC code
+- "low": The description is vague, ambiguous, or could match multiple SIC codes
+
+IMPORTANT:
+- Use the most specific code possible (4-digit Industry code if available)
+- Be conservative - if there's any ambiguity, use "low" confidence
+- Consider the full context including related industries and major groups
+"""
+
+    try:
+        response = await ctx.sample(
+            messages=analysis_prompt,
+            system_prompt=(
+                "You are a SIC classification expert. Analyze business descriptions "
+                "and match them to the most appropriate SIC code. "
+                "Return only valid JSON, no additional text or markdown."
+            ),
+            temperature=0.2,
+            max_tokens=500,
+        )
+
+        response_text = _extract_response_text(response)
+        result = json.loads(response_text)
+
+        # Normalize confidence to only high/low
+        conf = result.get("confidence", "low").lower()
+        result["confidence"] = "high" if conf == "high" else "low"
+
+        return result
+
+    except json.JSONDecodeError as e:
+        logger.error("Failed to parse LLM response: %s", str(e))
+        return {
+            "error": "Failed to parse classification response",
+            "raw_response": response_text if "response_text" in locals() else None,
+        }
+    except Exception as e:
+        logger.error("Classification analysis failed: %s", str(e))
+        return {"error": f"Classification failed: {str(e)}"}
+
+
+@mcp.tool()
+async def get_sic(prompt: str, ctx: Context) -> Dict[str, Any]:
+    """
+    Get the SIC (Standard Industrial Classification) code based on a business description.
+
+    This tool performs a vector search on the SIC taxonomy stored in Memgraph,
+    retrieves context about matching industry groups, and uses AI to determine
+    the most appropriate SIC code for the described business activity.
+
+    If the initial classification has low confidence, the tool will ask
+    clarifying questions to better understand the business activity.
+
+    Args:
+        prompt: A description of the business activity (e.g., "I work with private
+                citizens in a private bank providing them commercial credits")
+        ctx: FastMCP context for sampling
+
+    Returns:
+        Dictionary containing:
+        - selected_code: The best matching SIC code
+        - selected_name: Name of the classification
+        - confidence: Confidence level (high/low)
+        - explanation: Why this code was selected
+        - candidates: The raw candidate data used for selection
+    """
+    logger.info("get_sic called with prompt: %s", prompt)
+
+    # Step 1: Generate embedding for the user's prompt
+    logger.info("Generating embedding for prompt...")
+    query_embedding = get_embedding_from_text(prompt)
+
+    # Step 2: Perform vector search to find top 3 matching IndustryGroups
+    logger.info("Performing vector search...")
+    search_results = perform_vector_search(query_embedding, limit=3)
+
+    # Log vector search results
+    logger.info("Vector search returned %d results", len(search_results))
+    for i, result in enumerate(search_results):
+        props = result.get("properties", {})
+        logger.info(
+            "  Candidate %d: code=%s, name=%s, distance=%s",
+            i + 1,
+            props.get("code", "N/A"),
+            props.get("name", "N/A"),
+            result.get("distance", "N/A"),
+        )
+
+    if not search_results:
+        return {
+            "error": "No matching SIC classifications found",
+            "prompt": prompt,
+        }
+
+    if "error" in search_results[0]:
+        return {
+            "error": "Vector search failed",
+            "details": search_results[0].get("error"),
+            "prompt": prompt,
+        }
+
+    # Step 3: For each result, get the 1-hop neighborhood context
+    logger.info("Gathering context for %d candidates...", len(search_results))
+    candidates = []
+
+    for result in search_results:
+        properties = result.get("properties", {})
+        code = properties.get("code", "")
+
+        candidate = {
+            "code": code,
+            "name": properties.get("name", ""),
+            "context_text": properties.get("context_text", ""),
+            "distance": result.get("distance", 0),
+            "related_industries": [],
+            "major_group": None,
+        }
+
+        # Get node ID and fetch neighborhood
+        node_id = get_node_id_by_code(code)
+        if node_id is not None:
+            neighborhood = get_node_context(node_id, max_distance=1)
+
+            for neighbor in neighborhood:
+                # Classify neighbor by its properties
+                if "examples" in neighbor:
+                    # This is likely an Industry node
+                    candidate["related_industries"].append(
+                        {
+                            "code": neighbor.get("code", ""),
+                            "name": neighbor.get("name", ""),
+                            "description": neighbor.get("description", ""),
+                            "examples": neighbor.get("examples", []),
+                        }
+                    )
+                elif "description" in neighbor and "embedding" not in neighbor:
+                    # This is likely a MajorGroup node
+                    candidate["major_group"] = {
+                        "code": neighbor.get("code", ""),
+                        "name": neighbor.get("name", ""),
+                        "description": neighbor.get("description", ""),
+                    }
+
+        candidates.append(candidate)
+
+        # Log context gathered for this candidate
+        mg_code = None
+        if candidate["major_group"]:
+            mg_code = candidate["major_group"].get("code")
+        logger.info(
+            "  Context for %s: %d related industries, major_group=%s",
+            code,
+            len(candidate["related_industries"]),
+            mg_code,
+        )
+        for ind in candidate["related_industries"]:
+            logger.info(
+                "    Industry: %s - %s",
+                ind.get("code", ""),
+                ind.get("name", ""),
+            )
+
+    # Step 4: Initial LLM analysis
+    logger.info("Performing initial analysis with LLM...")
+    analysis_result = await analyze_and_select_sic_code(prompt, candidates, ctx)
+
+    # Check for errors
+    if "error" in analysis_result:
+        analysis_result["candidates"] = candidates
+        analysis_result["prompt"] = prompt
+        return analysis_result
+
+    # Step 5: If confidence is high, return immediately
+    if analysis_result.get("confidence") == "high":
+        logger.info("High confidence result, returning immediately")
+        analysis_result["candidates"] = candidates
+        analysis_result["prompt"] = prompt
+        return analysis_result
+
+    # Step 6: Low confidence - generate clarifying facts
+    logger.info("Low confidence, generating clarifying facts...")
+    facts_result = await generate_clarifying_facts(prompt, candidates, ctx)
+
+    if "error" in facts_result:
+        # Fallback to initial result if fact generation fails
+        logger.warning("Failed to generate facts, returning initial result")
+        analysis_result["candidates"] = candidates
+        analysis_result["prompt"] = prompt
+        analysis_result["clarification_failed"] = True
+        return analysis_result
+
+    # Step 7: Elicit user to select a fact
+    fact_1 = facts_result.get("fact_1", "Option 1")
+    fact_2 = facts_result.get("fact_2", "Option 2")
+    fact_3 = facts_result.get("fact_3", "Option 3")
+
+    elicit_message = (
+        "To better classify your business, please select the statement "
+        "that best describes your primary activity:\n\n"
+        f"1. {fact_1}\n\n"
+        f"2. {fact_2}\n\n"
+        f"3. {fact_3}"
+    )
+
+    logger.info("Eliciting user selection...")
+
+    try:
+        elicit_result = await ctx.elicit(
+            message=elicit_message,
+            response_type=["1", "2", "3"],
+        )
+
+        if elicit_result.action == "accept":
+            selected_option = elicit_result.data
+            logger.info("User selected option: %s", selected_option)
+
+            # Map selection to the fact
+            if selected_option == "1":
+                selected_fact = fact_1
+            elif selected_option == "2":
+                selected_fact = fact_2
+            else:
+                selected_fact = fact_3
+
+            # Step 8: Final analysis with user's selection
+            logger.info("Performing final analysis with user selection...")
+            final_result = await analyze_with_user_selection(
+                prompt, candidates, selected_fact, ctx
+            )
+
+            final_result["candidates"] = candidates
+            final_result["prompt"] = prompt
+            final_result["user_clarification"] = selected_fact
+            return final_result
+
+        elif elicit_result.action == "decline":
+            logger.info("User declined clarification")
+            analysis_result["candidates"] = candidates
+            analysis_result["prompt"] = prompt
+            analysis_result["user_declined_clarification"] = True
+            return analysis_result
+
+        else:  # cancel
+            logger.info("User cancelled")
+            return {
+                "status": "cancelled",
+                "message": "Classification cancelled by user",
+                "prompt": prompt,
+            }
+
+    except Exception as e:
+        logger.error("Elicitation failed: %s", str(e))
+        # Fallback to initial low-confidence result
+        analysis_result["candidates"] = candidates
+        analysis_result["prompt"] = prompt
+        analysis_result["elicitation_error"] = str(e)
+        return analysis_result
+
+
+logger.info("🏭 SIC Classification MCP server initialized")
+logger.info("Available tools: get_sic")
diff --git a/integrations/mcp-memgraph/uv.lock b/integrations/mcp-memgraph/uv.lock
index 8572a4c..b59e358 100644
--- a/integrations/mcp-memgraph/uv.lock
+++ b/integrations/mcp-memgraph/uv.lock
@@ -1276,6 +1276,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/dd/01/43f7b4eb61db3e565574c4c5714685d042fb652f9eef7e5a3de6aafa943a/jiter-0.11.1-cp314-cp314t-win_arm64.whl", hash = "sha256:28e4fdf2d7ebfc935523e50d1efa3970043cfaa161674fe66f9642409d001dfe", size = 188069 },
 ]
 
+[[package]]
+name = "joblib"
+version = "1.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e8/5d/447af5ea094b9e4c4054f82e223ada074c552335b9b4b2d14bd9b35a67c4/joblib-1.5.2.tar.gz", hash = "sha256:3faa5c39054b2f03ca547da9b2f52fde67c06240c31853f306aea97f13647b55", size = 331077 }
+wheels = [
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