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+# Tool Discovery in ToolUniverse
+
+## Overview
+
+ToolUniverse provides multiple methods to discover and search through 600+ scientific tools using natural language, keywords, or embeddings.
+
+## Discovery Methods
+
+### 1. Tool_Finder (Embedding-Based Search)
+
+Uses semantic embeddings to find relevant tools. **Requires GPU** for optimal performance.
+
+```python
+from tooluniverse import ToolUniverse
+
+tu = ToolUniverse()
+tu.load_tools()
+
+# Search by natural language description
+tools = tu.run({
+    "name": "Tool_Finder",
+    "arguments": {
+        "description": "protein structure prediction",
+        "limit": 10
+    }
+})
+
+print(tools)
+```
+
+**When to use:**
+- Natural language queries
+- Semantic similarity search
+- When GPU is available
+
+### 2. Tool_Finder_LLM (LLM-Based Search)
+
+Alternative to embedding-based search that uses LLM reasoning. **No GPU required**.
+
+```python
+tools = tu.run({
+    "name": "Tool_Finder_LLM",
+    "arguments": {
+        "description": "Find tools for analyzing gene expression data",
+        "limit": 10
+    }
+})
+```
+
+**When to use:**
+- When GPU is not available
+- Complex queries requiring reasoning
+- Semantic understanding needed
+
+### 3. Tool_Finder_Keyword (Keyword Search)
+
+Fast keyword-based search through tool names and descriptions.
+
+```python
+tools = tu.run({
+    "name": "Tool_Finder_Keyword",
+    "arguments": {
+        "description": "disease target associations",
+        "limit": 10
+    }
+})
+```
+
+**When to use:**
+- Fast searches
+- Known keywords
+- Exact term matching
+
+## Listing Available Tools
+
+### List All Tools
+```python
+all_tools = tu.list_tools()
+print(f"Total tools available: {len(all_tools)}")
+```
+
+### List Tools with Limit
+```python
+tools = tu.list_tools(limit=20)
+for tool in tools:
+    print(f"{tool['name']}: {tool['description']}")
+```
+
+## Tool Information
+
+### Get Tool Details
+```python
+# After finding a tool, inspect its details
+tool_info = tu.get_tool_info("OpenTargets_get_associated_targets_by_disease_efoId")
+print(tool_info)
+```
+
+## Search Strategies
+
+### By Domain
+Use domain-specific keywords:
+- Bioinformatics: "sequence alignment", "genomics", "RNA-seq"
+- Cheminformatics: "molecular dynamics", "drug design", "SMILES"
+- Machine Learning: "classification", "prediction", "neural network"
+- Structural Biology: "protein structure", "PDB", "crystallography"
+
+### By Functionality
+Search by what you want to accomplish:
+- "Find disease-gene associations"
+- "Predict protein interactions"
+- "Analyze clinical trial data"
+- "Generate molecular descriptors"
+
+### By Data Source
+Search for specific databases or APIs:
+- "OpenTargets", "PubChem", "UniProt"
+- "AlphaFold", "ChEMBL", "PDB"
+- "KEGG", "Reactome", "STRING"
+
+## Best Practices
+
+1. **Start Broad**: Begin with general terms, then refine
+2. **Use Multiple Methods**: Try different discovery methods if results aren't satisfactory
+3. **Set Appropriate Limits**: Use `limit` parameter to control result size (default: 10)
+4. **Check Tool Descriptions**: Review returned tool descriptions to verify relevance
+5. **Iterate**: Refine search terms based on initial results