diff --git a/lib/sanbase/mcp/server.ex b/lib/sanbase/mcp/server.ex
index 14d263110..44002f54d 100644
--- a/lib/sanbase/mcp/server.ex
+++ b/lib/sanbase/mcp/server.ex
@@ -27,6 +27,9 @@ defmodule Sanbase.MCP.Server do
   # Register Screener tool
   component(Sanbase.MCP.AssetsByMetricTool)
 
+  # Register Use Cases Catalog tool
+  component(Sanbase.MCP.UseCasesCatalogTool)
+
   if Application.compile_env(:sanbase, :env) in [:test, :dev] do
     IO.puts("Defining the extra MCP Server tools used in dev and test")
     # Some tools are enabled only in dev mode so we can test things during development
diff --git a/lib/sanbase/mcp/use_cases_catalog.ex b/lib/sanbase/mcp/use_cases_catalog.ex
new file mode 100644
index 000000000..bc8016713
--- /dev/null
+++ b/lib/sanbase/mcp/use_cases_catalog.ex
@@ -0,0 +1,153 @@
+defmodule Sanbase.MCP.UseCasesCatalog do
+  @moduledoc """
+  Catalog of analytical use cases with execution steps.
+  Each use case includes plain English instructions referencing specific MCP tools.
+  """
+
+  def all_use_cases do
+    [
+      identify_market_tops()
+    ]
+  end
+
+  defp identify_market_tops do
+    %{
+      id: "identify_market_tops",
+      title: "Identify Market Tops Using Santiment Metrics",
+      description: """
+      Multi-signal approach combining social volume, sentiment, network activity,
+      and on-chain metrics to identify potential market tops. This framework
+      emphasizes using multiple indicators together rather than relying on any
+      single metric.
+      """,
+      category: "market_timing",
+      difficulty: "intermediate",
+      estimated_time: "5-10 minutes",
+      applies_to: "Any crypto asset with sufficient data history",
+      steps: """
+      Step 1: Check for social volume spikes during rallies
+      Use the fetch_metric_data tool with these parameters:
+      - metric: "social_volume_total"
+      - slugs: [your target asset, e.g., "bitcoin"]
+      - time_period: "30d"
+      - interval: "1d"
+
+      Look for extreme spikes in social volume (3x or more above the 30-day baseline)
+      that occur during price rallies. This is especially reliable for mid-cap and
+      small-cap coins. Extreme social attention during rallies often marks local tops.
+
+      Step 2: Analyze sentiment for overbought crowd conditions
+      Use the fetch_metric_data tool to check positive sentiment:
+      - metric: "sentiment_positive_total"
+      - slugs: [your target asset]
+      - time_period: "7d"
+      - interval: "1d"
+
+      When the crowd is overly bullish, it can indicate a top. Look for:
+      - Positive sentiment representing > 70% of total mentions
+      - This elevated sentiment sustained for 3+ consecutive days
+
+      Optionally also fetch "sentiment_negative_total" and "sentiment_balance_total"
+      for a more complete picture. Strongly positive sentiment balance (>+0.5)
+      during rallies is a bearish signal.
+
+      Step 3: Check for network activity vs. price behavior divergence
+      Use the fetch_metric_data tool to check network activity:
+      - metric: "daily_active_addresses"
+      - slugs: [your target asset]
+      - time_period: "30d"
+      - interval: "1d"
+
+      Compare the trend in daily active addresses with price movement. If price has
+      risen 20% or more but daily active addresses remain flat or are declining,
+      this divergence signals a potential top. Healthy rallies are supported by
+      increasing on-chain activity.
+
+      Optionally also check "network_growth" (new addresses) for additional confirmation.
+
+      Step 4: Check MVRV ratio for overbought valuation
+      Use the fetch_metric_data tool to check valuation:
+      - metric: "mvrv_usd"
+      - slugs: [your target asset]
+      - time_period: "90d"
+      - interval: "1d"
+
+      MVRV (Market Value to Realized Value) ratio indicates whether holders are
+      in profit. High MVRV suggests many holders are sitting on gains and may
+      take profits. Thresholds vary by asset:
+      - Bitcoin: MVRV > 2.5 indicates overbought (bull market: > 3.5)
+      - Other assets: Research historical MVRV levels for the specific asset
+
+      Step 5: Check Mean Dollar Invested Age for long-term holder distribution
+      Use the fetch_metric_data tool to check coin age:
+      - metric: "mean_dollar_invested_age"
+      - slugs: [your target asset]
+      - time_period: "180d"
+      - interval: "1d"
+
+      MDIA tracks how long funds have stayed in addresses. Rising MDIA indicates
+      hodler accumulation, while dips suggest movement of previously idle coins.
+
+      Every major Bitcoin top has been accompanied by a significant drop in MDIA
+      as long-term holders distribute coins. Look for sharp drops (>10%) during
+      price rallies. This is particularly relevant for Bitcoin and major assets
+      with long history.
+      """,
+      interpretation: """
+      ## How to Interpret Combined Signals
+
+      This framework uses multiple indicators. Assess the overall picture:
+
+      **Strong Top Signal (High Confidence)**
+      When you observe 4-5 of these conditions together:
+      - Social volume spike 3x+ baseline during rally
+      - Positive sentiment > 70% sustained 3+ days
+      - Network activity declining while price rises 20%+
+      - MVRV > 2.5 (or asset-specific threshold)
+      - MDIA drops > 10% during rally
+
+      Action: Consider taking profits or tightening stop losses
+
+      **Moderate Top Signal**
+      When 2-3 bearish signals are present with mixed signals across categories.
+
+      Action: Monitor closely, consider reducing position size
+
+      **Weak/No Top Signal**
+      When 0-1 bearish signals present and most metrics show healthy conditions.
+
+      Action: Continue holding, no immediate concern
+
+      ## Important Context
+      - **Small/mid-cap coins**: Social volume spikes are more reliable indicators
+      - **Large-cap coins (BTC, ETH)**: MDIA and network activity more important
+      - **Bull markets**: Higher thresholds needed (MVRV > 3.5 for BTC)
+      - **Bear markets**: Lower thresholds (MVRV > 1.5 may indicate local top)
+      - **No single metric**: Always combine multiple data points for robust analysis
+
+      ## Setting Up Alerts
+      On Sanbase, you can subscribe to alerts for surges in social volume to catch
+      potential corrections early. Use the Social Trends tool to visualize momentum.
+      """,
+      references: [
+        %{
+          title: "Getting started with Santiment",
+          url: "https://academy.santiment.net/santiment-introduction/"
+        },
+        %{
+          title: "Getting started for traders",
+          url: "https://academy.santiment.net/for-traders/"
+        },
+        %{
+          title: "Understanding Short-Term Market Trends",
+          url:
+            "https://academy.santiment.net/education-and-use-cases/understanding-short-term-market-trends/"
+        },
+        %{
+          title: "Sentiment metrics",
+          url: "https://academy.santiment.net/metrics/sentiment-metrics/"
+        }
+      ]
+    }
+  end
+end
diff --git a/lib/sanbase/mcp/use_cases_catalog_tool.ex b/lib/sanbase/mcp/use_cases_catalog_tool.ex
new file mode 100644
index 000000000..34f0c58d7
--- /dev/null
+++ b/lib/sanbase/mcp/use_cases_catalog_tool.ex
@@ -0,0 +1,76 @@
+defmodule Sanbase.MCP.UseCasesCatalogTool do
+  @moduledoc """
+  **CALL THIS TOOL FIRST** when answering crypto analysis questions to check if the query
+  matches any predefined analytical strategies.
+
+  This tool returns a catalog of proven analytical use cases with complete step-by-step
+  execution instructions. Each use case is a ready-to-use analytical recipe that:
+  - Identifies the analytical goal (e.g., "Is this asset near a top?")
+  - Provides 5-10 detailed steps referencing specific MCP tools to call
+  - Explains how to interpret the combined results
+  - Ensures comprehensive multi-signal analysis following best practices
+
+  ## When to Use This Tool
+
+  **ALWAYS call this tool FIRST** when the user asks questions like:
+  - "Is [asset] near a top?" or "Is [asset] overbought?"
+  - "Should I buy/sell [asset]?"
+  - "Is [asset] in an accumulation zone?"
+  - "What's the market sentiment for [asset]?"
+  - Any question about market timing, price predictions, or trading decisions
+
+  ## How to Use This Tool
+
+  1. **Call this tool first** (no parameters needed)
+  2. **Compare** the user's query to the available use case titles and descriptions
+  3. **If a use case matches**: Follow the step-by-step instructions provided
+     - Each step tells you exactly which tool to call and with what parameters
+     - Execute the steps in order, gathering data from each
+     - Synthesize results using the interpretation guide
+  4. **If no use case matches**: Proceed with ad-hoc analysis using individual tools
+
+  ## Benefits of Using This Tool First
+
+  - **Comprehensive analysis**: Use cases combine multiple signals (not just one metric)
+  - **Best practices**: Strategies are based on proven analytical frameworks
+  - **Time-saving**: Get a complete analytical recipe instead of guessing which metrics to use
+  - **Better answers**: Multi-signal approaches provide more reliable insights
+
+  ## Example Use Cases Available
+
+  - **Identify Market Tops**: Combines social volume, sentiment, network activity, MVRV,
+    and mean dollar age to detect potential tops with high confidence
+  - More use cases will be added over time
+
+  ## Response Format
+
+  Returns a list of use cases, each containing:
+  - `title`: What analytical question this use case answers
+  - `steps`: Plain text instructions with specific tool calls and parameters
+  - `interpretation`: How to combine and interpret the results
+  """
+
+  use Anubis.Server.Component, type: :tool
+
+  alias Anubis.Server.Response
+  alias Sanbase.MCP.UseCasesCatalog
+
+  schema do
+  end
+
+  @impl true
+  def execute(_params, frame) do
+    use_cases = UseCasesCatalog.all_use_cases()
+
+    simplified_use_cases =
+      Enum.map(use_cases, fn use_case ->
+        %{
+          title: use_case.title,
+          steps: use_case.steps,
+          interpretation: use_case.interpretation
+        }
+      end)
+
+    {:reply, Response.json(Response.tool(), simplified_use_cases), frame}
+  end
+end
diff --git a/test/sanbase/mcp/use_cases_catalog_tool_test.exs b/test/sanbase/mcp/use_cases_catalog_tool_test.exs
new file mode 100644
index 000000000..0f92a44c0
--- /dev/null
+++ b/test/sanbase/mcp/use_cases_catalog_tool_test.exs
@@ -0,0 +1,82 @@
+defmodule Sanbase.MCP.UseCasesCatalogToolTest do
+  use SanbaseWeb.ConnCase, async: false
+
+  alias Sanbase.MCP.UseCasesCatalog
+
+  describe "UseCasesCatalog" do
+    test "returns all use cases with required fields" do
+      use_cases = UseCasesCatalog.all_use_cases()
+
+      assert is_list(use_cases)
+      assert length(use_cases) > 0
+
+      Enum.each(use_cases, fn use_case ->
+        assert use_case.title
+        assert is_binary(use_case.steps)
+        assert String.length(use_case.steps) > 100
+        assert use_case.interpretation
+        assert String.contains?(use_case.steps, "Step 1")
+      end)
+    end
+
+    test "steps mention tools to use" do
+      use_cases = UseCasesCatalog.all_use_cases()
+
+      Enum.each(use_cases, fn use_case ->
+        assert String.contains?(use_case.steps, "tool") or
+                 String.contains?(use_case.steps, "fetch_metric_data")
+      end)
+    end
+  end
+
+  describe "UseCasesCatalogTool execute/2" do
+    test "returns simplified list of use cases" do
+      frame = %{assigns: %{}}
+
+      {:reply, response, _frame} = Sanbase.MCP.UseCasesCatalogTool.execute(%{}, frame)
+
+      assert response.content
+      [%{"text" => json_text, "type" => "text"}] = response.content
+      use_cases = Jason.decode!(json_text)
+
+      assert is_list(use_cases)
+      assert length(use_cases) > 0
+    end
+
+    test "returned use cases have only title, steps, and interpretation" do
+      frame = %{assigns: %{}}
+
+      {:reply, response, _frame} = Sanbase.MCP.UseCasesCatalogTool.execute(%{}, frame)
+
+      [%{"text" => json_text, "type" => "text"}] = response.content
+      use_cases = Jason.decode!(json_text)
+      use_case = List.first(use_cases)
+
+      assert use_case["title"]
+      assert use_case["steps"]
+      assert use_case["interpretation"]
+
+      assert is_binary(use_case["steps"])
+      assert String.length(use_case["steps"]) > 100
+
+      refute Map.has_key?(use_case, "id")
+      refute Map.has_key?(use_case, "description")
+      refute Map.has_key?(use_case, "category")
+      refute Map.has_key?(use_case, "metadata")
+    end
+
+    test "steps is plain text not a list" do
+      frame = %{assigns: %{}}
+
+      {:reply, response, _frame} = Sanbase.MCP.UseCasesCatalogTool.execute(%{}, frame)
+
+      [%{"text" => json_text, "type" => "text"}] = response.content
+      use_cases = Jason.decode!(json_text)
+
+      Enum.each(use_cases, fn use_case ->
+        assert is_binary(use_case["steps"])
+        refute is_list(use_case["steps"])
+      end)
+    end
+  end
+end