diff --git a/README.md b/README.md
index d2279e5..1c5105e 100644
--- a/README.md
+++ b/README.md
@@ -17,18 +17,44 @@ from stackone_ai import StackOneToolSet
 toolset = StackOneToolSet()  # Uses STACKONE_API_KEY env var
 # Or explicitly: toolset = StackOneToolSet(api_key="your-api-key")
 
-# Get HRIS-related tools
+# Get HRIS-related tools with glob patterns
 tools = toolset.get_tools("hris_*", account_id="your-account-id")
+# Exclude certain tools with negative patterns
+tools = toolset.get_tools(["hris_*", "!hris_delete_*"])
 
-# Use a specific tool
+# Use a specific tool with the new call method
 employee_tool = tools.get_tool("hris_get_employee")
+# Call with keyword arguments
+employee = employee_tool.call(id="employee-id")
+# Or with traditional execute method
 employee = employee_tool.execute({"id": "employee-id"})
 ```
 
+## Meta Tools (Beta)
+
+Meta tools enable dynamic tool discovery and execution without hardcoding tool names:
+
+```python
+# Get meta tools for dynamic discovery
+tools = toolset.get_tools("hris_*")
+meta_tools = tools.meta_tools()
+
+# Search for relevant tools using natural language
+filter_tool = meta_tools.get_tool("meta_filter_relevant_tools")
+results = filter_tool.call(query="manage employees", limit=5)
+
+# Execute discovered tools dynamically
+execute_tool = meta_tools.get_tool("meta_execute_tool")
+result = execute_tool.call(toolName="hris_list_employees", params={"limit": 10})
+```
+
 ## Features
 
 - Unified interface for multiple SaaS tools
 - AI-friendly tool descriptions and parameters
+- **Tool Calling**: Direct method calling with `tool.call()` for intuitive usage
+- **Glob Pattern Filtering**: Advanced tool filtering with patterns like `"hris_*"` and exclusions `"!hris_delete_*"`
+- **Meta Tools** (Beta): Dynamic tool discovery and execution based on natural language queries
 - Integration with popular AI frameworks:
   - OpenAI Functions
   - LangChain Tools
diff --git a/examples/meta_tools_example.py b/examples/meta_tools_example.py
new file mode 100644
index 0000000..069c9cd
--- /dev/null
+++ b/examples/meta_tools_example.py
@@ -0,0 +1,247 @@
+#!/usr/bin/env python
+"""
+Example demonstrating meta tools for dynamic tool discovery and execution.
+
+Meta tools allow AI agents to search for relevant tools based on natural language queries
+and execute them dynamically without hardcoding tool names.
+"""
+
+import os
+
+from dotenv import load_dotenv
+
+from stackone_ai import StackOneToolSet
+
+# Load environment variables
+load_dotenv()
+
+
+def example_meta_tools_basic():
+    """Basic example of using meta tools for tool discovery"""
+    print("🔍 Example 1: Dynamic tool discovery\n")
+
+    # Initialize StackOne toolset
+    toolset = StackOneToolSet()
+
+    # Get all available tools (you can also use pattern like "hris_*")
+    all_tools = toolset.get_tools("hris_*")
+    print(f"Total HRIS tools available: {len(all_tools)}")
+
+    # Get meta tools for dynamic discovery
+    meta_tools = all_tools.meta_tools()
+
+    # Get the filter tool to search for relevant tools
+    filter_tool = meta_tools.get_tool("meta_filter_relevant_tools")
+    if filter_tool:
+        # Search for employee management tools
+        result = filter_tool.call(query="manage employees create update list", limit=5, minScore=0.0)
+
+        print("Found relevant tools:")
+        for tool in result.get("tools", []):
+            print(f"  - {tool['name']} (score: {tool['score']:.2f}): {tool['description']}")
+
+    print()
+
+
+def example_meta_tools_with_execution():
+    """Example of discovering and executing tools dynamically"""
+    print("🚀 Example 2: Dynamic tool execution\n")
+
+    # Initialize toolset
+    toolset = StackOneToolSet()
+
+    # Get all tools
+    all_tools = toolset.get_tools()
+    meta_tools = all_tools.meta_tools()
+
+    # Step 1: Search for relevant tools
+    filter_tool = meta_tools.get_tool("meta_filter_relevant_tools")
+    execute_tool = meta_tools.get_tool("meta_execute_tool")
+
+    if filter_tool and execute_tool:
+        # Find tools for listing employees
+        search_result = filter_tool.call(query="list all employees", limit=1)
+
+        tools_found = search_result.get("tools", [])
+        if tools_found:
+            best_tool = tools_found[0]
+            print(f"Best matching tool: {best_tool['name']}")
+            print(f"Description: {best_tool['description']}")
+            print(f"Relevance score: {best_tool['score']:.2f}")
+
+            # Step 2: Execute the found tool
+            try:
+                print(f"\nExecuting {best_tool['name']}...")
+                result = execute_tool.call(toolName=best_tool["name"], params={"limit": 5})
+                print(f"Execution result: {result}")
+            except Exception as e:
+                print(f"Execution failed (expected in example): {e}")
+
+    print()
+
+
+def example_tool_calling():
+    """Example of the new tool calling functionality"""
+    print("📞 Example 3: Tool calling functionality\n")
+
+    # Initialize toolset
+    toolset = StackOneToolSet()
+
+    # Get a specific tool
+    tool = toolset.get_tool("hris_list_employees")
+
+    if tool:
+        print(f"Tool: {tool.name}")
+        print(f"Description: {tool.description}")
+
+        # New calling methods
+        try:
+            # Method 1: Call with keyword arguments
+            result = tool.call(limit=10, page=1)
+            print(f"Called with kwargs: {result}")
+        except Exception as e:
+            print(f"Call with kwargs (expected to fail in example): {e}")
+
+        try:
+            # Method 2: Call with dictionary
+            result = tool.call({"limit": 10, "page": 1})
+            print(f"Called with dict: {result}")
+        except Exception as e:
+            print(f"Call with dict (expected to fail in example): {e}")
+
+    print()
+
+
+def example_with_openai():
+    """Example of using meta tools with OpenAI"""
+    print("🤖 Example 4: Using meta tools with OpenAI\n")
+
+    try:
+        from openai import OpenAI
+
+        # Initialize OpenAI client
+        client = OpenAI()
+
+        # Initialize StackOne toolset
+        toolset = StackOneToolSet()
+
+        # Get HRIS tools and their meta tools
+        hris_tools = toolset.get_tools("hris_*")
+        meta_tools = hris_tools.meta_tools()
+
+        # Convert to OpenAI format
+        openai_tools = meta_tools.to_openai()
+
+        # Create a chat completion with meta tools
+        response = client.chat.completions.create(
+            model="gpt-4",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are an HR assistant. Use meta_filter_relevant_tools to find appropriate tools, then meta_execute_tool to execute them.",
+                },
+                {"role": "user", "content": "Can you help me find tools for managing employee records?"},
+            ],
+            tools=openai_tools,
+            tool_choice="auto",
+        )
+
+        print("OpenAI Response:", response.choices[0].message.content)
+
+        if response.choices[0].message.tool_calls:
+            print("\nTool calls made:")
+            for tool_call in response.choices[0].message.tool_calls:
+                print(f"  - {tool_call.function.name}")
+
+    except ImportError:
+        print("OpenAI library not installed. Install with: pip install openai")
+    except Exception as e:
+        print(f"OpenAI example failed: {e}")
+
+    print()
+
+
+def example_with_langchain():
+    """Example of using tools with LangChain"""
+    print("🔗 Example 5: Using tools with LangChain\n")
+
+    try:
+        from langchain.agents import AgentExecutor, create_tool_calling_agent
+        from langchain_core.prompts import ChatPromptTemplate
+        from langchain_openai import ChatOpenAI
+
+        # Initialize StackOne toolset
+        toolset = StackOneToolSet()
+
+        # Get tools and convert to LangChain format
+        tools = toolset.get_tools("hris_list_*")
+        langchain_tools = tools.to_langchain()
+
+        # Get meta tools as well
+        meta_tools = tools.meta_tools()
+        langchain_meta_tools = meta_tools.to_langchain()
+
+        # Combine all tools
+        all_langchain_tools = list(langchain_tools) + list(langchain_meta_tools)
+
+        print(f"Available tools for LangChain: {len(all_langchain_tools)}")
+        for tool in all_langchain_tools:
+            print(f"  - {tool.name}: {tool.description}")
+
+        # Create LangChain agent
+        llm = ChatOpenAI(model="gpt-4", temperature=0)
+
+        prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    "You are an HR assistant. Use the meta tools to discover and execute relevant tools.",
+                ),
+                ("human", "{input}"),
+                ("placeholder", "{agent_scratchpad}"),
+            ]
+        )
+
+        agent = create_tool_calling_agent(llm, all_langchain_tools, prompt)
+        agent_executor = AgentExecutor(agent=agent, tools=all_langchain_tools, verbose=True)
+
+        # Run the agent
+        result = agent_executor.invoke({"input": "Find tools that can list employee data"})
+
+        print(f"\nAgent result: {result['output']}")
+
+    except ImportError as e:
+        print(f"LangChain dependencies not installed: {e}")
+        print("Install with: pip install langchain-openai")
+    except Exception as e:
+        print(f"LangChain example failed: {e}")
+
+    print()
+
+
+def main():
+    """Run all examples"""
+    print("=" * 60)
+    print("StackOne AI SDK - Meta Tools & Tool Calling Examples")
+    print("=" * 60)
+    print()
+
+    # Basic examples that work without external APIs
+    example_meta_tools_basic()
+    example_meta_tools_with_execution()
+    example_tool_calling()
+
+    # Examples that require OpenAI API
+    if os.getenv("OPENAI_API_KEY"):
+        example_with_openai()
+        example_with_langchain()
+    else:
+        print("ℹ️  Set OPENAI_API_KEY to run OpenAI and LangChain examples\n")
+
+    print("=" * 60)
+    print("Examples completed!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pyproject.toml b/pyproject.toml
index bb1d9b4..31b5d14 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,6 +20,8 @@ dependencies = [
     "requests>=2.32.3",
     "langchain-core>=0.1.0",
     "mcp[cli]>=1.3.0",
+    "bm25s>=0.2.2",
+    "numpy>=1.24.0",
 ]
 
 [project.scripts]
@@ -104,3 +106,7 @@ warn_redundant_casts = true
 warn_unused_ignores = true
 warn_return_any = true
 warn_unreachable = true
+
+[[tool.mypy.overrides]]
+module = "bm25s"
+ignore_missing_imports = true
diff --git a/stackone_ai/__init__.py b/stackone_ai/__init__.py
index e2b99aa..bc0cff1 100644
--- a/stackone_ai/__init__.py
+++ b/stackone_ai/__init__.py
@@ -1,6 +1,7 @@
 """StackOne AI SDK"""
 
+from .models import StackOneTool, Tools
 from .toolset import StackOneToolSet
 
-__all__ = ["StackOneToolSet"]
+__all__ = ["StackOneToolSet", "StackOneTool", "Tools"]
 __version__ = "0.0.4"
diff --git a/stackone_ai/meta_tools.py b/stackone_ai/meta_tools.py
new file mode 100644
index 0000000..49469f3
--- /dev/null
+++ b/stackone_ai/meta_tools.py
@@ -0,0 +1,277 @@
+"""Meta tools for dynamic tool discovery and execution"""
+
+from __future__ import annotations
+
+import json
+from typing import TYPE_CHECKING
+
+import bm25s
+import numpy as np
+from pydantic import BaseModel
+
+from stackone_ai.models import ExecuteConfig, JsonDict, StackOneTool, ToolParameters
+
+if TYPE_CHECKING:
+    from stackone_ai.models import Tools
+
+
+class MetaToolSearchResult(BaseModel):
+    """Result from meta_filter_relevant_tools"""
+
+    name: str
+    description: str
+    score: float
+
+
+class ToolIndex:
+    """BM25-based tool search index"""
+
+    def __init__(self, tools: list[StackOneTool]) -> None:
+        self.tools = tools
+        self.tool_map = {tool.name: tool for tool in tools}
+
+        # Prepare corpus for BM25
+        corpus = []
+        self.tool_names = []
+
+        for tool in tools:
+            # Extract category and action from tool name
+            parts = tool.name.split("_")
+            category = parts[0] if parts else ""
+
+            # Extract action types
+            action_types = ["create", "update", "delete", "get", "list", "search"]
+            actions = [p for p in parts if p in action_types]
+
+            # Combine name, description, category and tags for indexing
+            doc_text = " ".join(
+                [
+                    tool.name,
+                    tool.description,
+                    category,
+                    " ".join(parts),
+                    " ".join(actions),
+                ]
+            )
+
+            corpus.append(doc_text)
+            self.tool_names.append(tool.name)
+
+        # Create BM25 index
+        self.retriever = bm25s.BM25()
+        # Tokenize without stemming for simplicity
+        corpus_tokens = bm25s.tokenize(corpus, stemmer=None, show_progress=False)
+        self.retriever.index(corpus_tokens)
+
+    def search(self, query: str, limit: int = 5, min_score: float = 0.0) -> list[MetaToolSearchResult]:
+        """Search for relevant tools using BM25
+
+        Args:
+            query: Natural language query
+            limit: Maximum number of results
+            min_score: Minimum relevance score (0-1)
+
+        Returns:
+            List of search results sorted by relevance
+        """
+        # Tokenize query
+        query_tokens = bm25s.tokenize([query], stemmer=None, show_progress=False)
+
+        # Search with BM25
+        results, scores = self.retriever.retrieve(query_tokens, k=min(limit * 2, len(self.tools)))
+
+        # Process results
+        search_results = []
+        for idx, score in zip(results[0], scores[0], strict=False):
+            if score < min_score:
+                continue
+
+            tool_name = self.tool_names[idx]
+            tool = self.tool_map[tool_name]
+
+            # Normalize score to 0-1 range
+            normalized_score = float(1 / (1 + np.exp(-score / 10)))
+
+            search_results.append(
+                MetaToolSearchResult(
+                    name=tool.name,
+                    description=tool.description,
+                    score=normalized_score,
+                )
+            )
+
+            if len(search_results) >= limit:
+                break
+
+        return search_results
+
+
+def create_meta_filter_tool(index: ToolIndex) -> StackOneTool:
+    """Create the meta_filter_relevant_tools tool
+
+    Args:
+        index: Tool search index
+
+    Returns:
+        Meta tool for filtering relevant tools
+    """
+    name = "meta_filter_relevant_tools"
+    description = (
+        "Searches for relevant tools based on a natural language query. "
+        "This tool should be called first to discover available tools before executing them."
+    )
+
+    parameters = ToolParameters(
+        type="object",
+        properties={
+            "query": {
+                "type": "string",
+                "description": (
+                    "Natural language query describing what tools you need "
+                    '(e.g., "tools for managing employees", "create time off request")'
+                ),
+            },
+            "limit": {
+                "type": "number",
+                "description": "Maximum number of tools to return (default: 5)",
+                "default": 5,
+            },
+            "minScore": {
+                "type": "number",
+                "description": "Minimum relevance score (0-1) to filter results (default: 0.0)",
+                "default": 0.0,
+            },
+        },
+    )
+
+    def execute_filter(arguments: str | JsonDict | None = None) -> JsonDict:
+        """Execute the filter tool"""
+        # Parse arguments
+        if isinstance(arguments, str):
+            kwargs = json.loads(arguments)
+        else:
+            kwargs = arguments or {}
+
+        query = kwargs.get("query", "")
+        limit = int(kwargs.get("limit", 5))
+        min_score = float(kwargs.get("minScore", 0.0))
+
+        # Search for tools
+        results = index.search(query, limit, min_score)
+
+        # Format results
+        tools_data = [
+            {
+                "name": r.name,
+                "description": r.description,
+                "score": r.score,
+            }
+            for r in results
+        ]
+
+        return {"tools": tools_data}
+
+    # Create execute config for the meta tool
+    execute_config = ExecuteConfig(
+        name=name,
+        method="POST",
+        url="",  # Meta tools don't make HTTP requests
+        headers={},
+    )
+
+    # Create a wrapper class that delegates execute to our custom function
+    class MetaFilterTool(StackOneTool):
+        """Meta tool for filtering relevant tools"""
+
+        def __init__(self) -> None:
+            super().__init__(
+                description=description,
+                parameters=parameters,
+                _execute_config=execute_config,
+                _api_key="",  # Meta tools don't need API key
+                _account_id=None,
+            )
+
+        def execute(self, arguments: str | JsonDict | None = None) -> JsonDict:
+            return execute_filter(arguments)
+
+    return MetaFilterTool()
+
+
+def create_meta_execute_tool(tools_collection: Tools) -> StackOneTool:
+    """Create the meta_execute_tool
+
+    Args:
+        tools_collection: Collection of tools to execute from
+
+    Returns:
+        Meta tool for executing discovered tools
+    """
+    name = "meta_execute_tool"
+    description = (
+        "Executes a tool by name with the provided parameters. "
+        "Use this after discovering tools with meta_filter_relevant_tools."
+    )
+
+    parameters = ToolParameters(
+        type="object",
+        properties={
+            "toolName": {
+                "type": "string",
+                "description": "Name of the tool to execute",
+            },
+            "params": {
+                "type": "object",
+                "description": "Parameters to pass to the tool",
+                "additionalProperties": True,
+            },
+        },
+    )
+
+    def execute_tool(arguments: str | JsonDict | None = None) -> JsonDict:
+        """Execute the meta execute tool"""
+        # Parse arguments
+        if isinstance(arguments, str):
+            kwargs = json.loads(arguments)
+        else:
+            kwargs = arguments or {}
+
+        tool_name = kwargs.get("toolName")
+        params = kwargs.get("params", {})
+
+        if not tool_name:
+            raise ValueError("toolName is required")
+
+        # Get the tool
+        tool = tools_collection.get_tool(tool_name)
+        if not tool:
+            raise ValueError(f"Tool '{tool_name}' not found")
+
+        # Execute the tool
+        return tool.execute(params)
+
+    # Create execute config for the meta tool
+    execute_config = ExecuteConfig(
+        name=name,
+        method="POST",
+        url="",  # Meta tools don't make HTTP requests
+        headers={},
+    )
+
+    # Create a wrapper class that delegates execute to our custom function
+    class MetaExecuteTool(StackOneTool):
+        """Meta tool for executing discovered tools"""
+
+        def __init__(self) -> None:
+            super().__init__(
+                description=description,
+                parameters=parameters,
+                _execute_config=execute_config,
+                _api_key="",  # Meta tools don't need API key
+                _account_id=None,
+            )
+
+        def execute(self, arguments: str | JsonDict | None = None) -> JsonDict:
+            return execute_tool(arguments)
+
+    return MetaExecuteTool()
diff --git a/stackone_ai/models.py b/stackone_ai/models.py
index 52bc2ae..6194c9e 100644
--- a/stackone_ai/models.py
+++ b/stackone_ai/models.py
@@ -1,7 +1,9 @@
+import asyncio
 import base64
 import json
 from collections.abc import Sequence
 from enum import Enum
+from functools import partial
 from typing import Annotated, Any, TypeAlias, cast
 
 import requests
@@ -214,6 +216,66 @@ def execute(self, arguments: str | JsonDict | None = None) -> JsonDict:
                 ) from e
             raise StackOneError(f"Request failed: {e}") from e
 
+    def call(self, *args: Any, **kwargs: Any) -> JsonDict:
+        """Call the tool with the given arguments
+
+        This method provides a more intuitive way to execute tools directly.
+
+        Args:
+            *args: If a single argument is provided, it's treated as the full arguments dict/string
+            **kwargs: Keyword arguments to pass to the tool
+
+        Returns:
+            API response as dict
+
+        Raises:
+            StackOneAPIError: If the API request fails
+            ValueError: If the arguments are invalid
+
+        Examples:
+            >>> tool.call({"name": "John", "email": "john@example.com"})
+            >>> tool.call(name="John", email="john@example.com")
+        """
+        if args and kwargs:
+            raise ValueError("Cannot provide both positional and keyword arguments")
+
+        if args:
+            if len(args) > 1:
+                raise ValueError("Only one positional argument is allowed")
+            return self.execute(args[0])
+
+        return self.execute(kwargs if kwargs else None)
+
+    async def acall(self, *args: Any, **kwargs: Any) -> JsonDict:
+        """Async version of call method
+
+        Args:
+            *args: If a single argument is provided, it's treated as the full arguments dict/string
+            **kwargs: Keyword arguments to pass to the tool
+
+        Returns:
+            API response as dict
+
+        Raises:
+            StackOneAPIError: If the API request fails
+            ValueError: If the arguments are invalid
+        """
+        # For now, we'll use asyncio to run the sync version
+        # In the future, this should use aiohttp for true async
+
+        # Create a partial function with the arguments
+        if args and kwargs:
+            raise ValueError("Cannot provide both positional and keyword arguments")
+
+        if args:
+            if len(args) > 1:
+                raise ValueError("Only one positional argument is allowed")
+            func = partial(self.execute, args[0])
+        else:
+            func = partial(self.execute, kwargs if kwargs else None)
+
+        return await asyncio.get_event_loop().run_in_executor(None, func)
+
     def to_openai_function(self) -> JsonDict:
         """Convert this tool to OpenAI's function format
 
@@ -417,3 +479,29 @@ def to_langchain(self) -> Sequence[BaseTool]:
             Sequence of tools in LangChain format
         """
         return [tool.to_langchain() for tool in self.tools]
+
+    def meta_tools(self) -> "Tools":
+        """Return meta tools for tool discovery and execution
+
+        Meta tools enable dynamic tool discovery and execution based on natural language queries.
+
+        Returns:
+            Tools collection containing meta_filter_relevant_tools and meta_execute_tool
+
+        Note:
+            This feature is in beta and may change in future versions
+        """
+        from stackone_ai.meta_tools import (
+            ToolIndex,
+            create_meta_execute_tool,
+            create_meta_filter_tool,
+        )
+
+        # Create search index
+        index = ToolIndex(self.tools)
+
+        # Create meta tools
+        filter_tool = create_meta_filter_tool(index)
+        execute_tool = create_meta_execute_tool(self)
+
+        return Tools([filter_tool, execute_tool])
diff --git a/tests/test_meta_tools.py b/tests/test_meta_tools.py
new file mode 100644
index 0000000..f5ac989
--- /dev/null
+++ b/tests/test_meta_tools.py
@@ -0,0 +1,264 @@
+"""Tests for meta tools functionality"""
+
+import pytest
+import responses
+
+from stackone_ai import StackOneTool, Tools
+from stackone_ai.meta_tools import ToolIndex, create_meta_execute_tool, create_meta_filter_tool
+from stackone_ai.models import ExecuteConfig, ToolParameters
+
+
+@pytest.fixture
+def sample_tools():
+    """Create sample tools for testing"""
+    tools = []
+
+    # Create HRIS tools
+    for action in ["create", "list", "update", "delete"]:
+        for entity in ["employee", "department", "timeoff"]:
+            tool_name = f"hris_{action}_{entity}"
+            execute_config = ExecuteConfig(
+                name=tool_name,
+                method="POST" if action in ["create", "update"] else "GET",
+                url=f"https://api.example.com/hris/{entity}",
+                headers={},
+            )
+
+            parameters = ToolParameters(
+                type="object",
+                properties={
+                    "id": {"type": "string", "description": "Entity ID"},
+                    "data": {"type": "object", "description": "Entity data"},
+                },
+            )
+
+            tool = StackOneTool(
+                description=f"{action.capitalize()} {entity} in HRIS system",
+                parameters=parameters,
+                _execute_config=execute_config,
+                _api_key="test_key",
+            )
+            tools.append(tool)
+
+    # Create ATS tools
+    for action in ["create", "list", "search"]:
+        for entity in ["candidate", "job", "application"]:
+            tool_name = f"ats_{action}_{entity}"
+            execute_config = ExecuteConfig(
+                name=tool_name,
+                method="POST" if action == "create" else "GET",
+                url=f"https://api.example.com/ats/{entity}",
+                headers={},
+            )
+
+            parameters = ToolParameters(
+                type="object",
+                properties={
+                    "query": {"type": "string", "description": "Search query"},
+                    "filters": {"type": "object", "description": "Filter criteria"},
+                },
+            )
+
+            tool = StackOneTool(
+                description=f"{action.capitalize()} {entity} in ATS system",
+                parameters=parameters,
+                _execute_config=execute_config,
+                _api_key="test_key",
+            )
+            tools.append(tool)
+
+    return tools
+
+
+@pytest.fixture
+def tools_collection(sample_tools):
+    """Create a Tools collection from sample tools"""
+    return Tools(sample_tools)
+
+
+class TestToolIndex:
+    """Test the BM25 tool search index"""
+
+    def test_index_creation(self, sample_tools):
+        """Test creating a tool index"""
+        index = ToolIndex(sample_tools)
+        assert len(index.tools) == len(sample_tools)
+        assert len(index.tool_map) == len(sample_tools)
+
+    def test_search_basic(self, sample_tools):
+        """Test basic search functionality"""
+        index = ToolIndex(sample_tools)
+
+        # Search for employee-related tools
+        results = index.search("employee", limit=5)
+
+        assert len(results) > 0
+        # Check that at least one result contains "employee"
+        assert any("employee" in r.name for r in results)
+
+    def test_search_with_action(self, sample_tools):
+        """Test searching with action keywords"""
+        index = ToolIndex(sample_tools)
+
+        # Search for create operations
+        results = index.search("create new", limit=5)
+
+        assert len(results) > 0
+        # Most results should contain "create"
+        create_tools = [r for r in results if "create" in r.name]
+        assert len(create_tools) > 0
+
+    def test_search_with_min_score(self, sample_tools):
+        """Test filtering by minimum score"""
+        index = ToolIndex(sample_tools)
+
+        # Search with a high min_score
+        results = index.search("employee", limit=10, min_score=0.5)
+
+        # All results should have score >= 0.5
+        assert all(r.score >= 0.5 for r in results)
+
+    def test_search_limit(self, sample_tools):
+        """Test limiting search results"""
+        index = ToolIndex(sample_tools)
+
+        # Search with limit
+        results = index.search("", limit=3)
+
+        assert len(results) <= 3
+
+
+class TestMetaFilterTool:
+    """Test the meta_filter_relevant_tools functionality"""
+
+    def test_filter_tool_creation(self, sample_tools):
+        """Test creating the filter tool"""
+        index = ToolIndex(sample_tools)
+        filter_tool = create_meta_filter_tool(index)
+
+        assert filter_tool.name == "meta_filter_relevant_tools"
+        assert "natural language query" in filter_tool.description.lower()
+
+    def test_filter_tool_execute(self, sample_tools):
+        """Test executing the filter tool"""
+        index = ToolIndex(sample_tools)
+        filter_tool = create_meta_filter_tool(index)
+
+        # Execute with a query
+        result = filter_tool.execute(
+            {
+                "query": "manage employees",
+                "limit": 3,
+                "minScore": 0.0,
+            }
+        )
+
+        assert "tools" in result
+        assert isinstance(result["tools"], list)
+        assert len(result["tools"]) <= 3
+
+        # Check tool structure
+        if result["tools"]:
+            tool = result["tools"][0]
+            assert "name" in tool
+            assert "description" in tool
+            assert "score" in tool
+
+    def test_filter_tool_call(self, sample_tools):
+        """Test calling the filter tool with call method"""
+        index = ToolIndex(sample_tools)
+        filter_tool = create_meta_filter_tool(index)
+
+        # Call with kwargs
+        result = filter_tool.call(query="candidate", limit=2)
+
+        assert "tools" in result
+        assert len(result["tools"]) <= 2
+
+
+class TestMetaExecuteTool:
+    """Test the meta_execute_tool functionality"""
+
+    def test_execute_tool_creation(self, tools_collection):
+        """Test creating the execute tool"""
+        execute_tool = create_meta_execute_tool(tools_collection)
+
+        assert execute_tool.name == "meta_execute_tool"
+        assert "executes a tool" in execute_tool.description.lower()
+
+    def test_execute_tool_missing_name(self, tools_collection):
+        """Test execute tool with missing tool name"""
+        execute_tool = create_meta_execute_tool(tools_collection)
+
+        with pytest.raises(ValueError, match="toolName is required"):
+            execute_tool.execute({"params": {}})
+
+    def test_execute_tool_invalid_name(self, tools_collection):
+        """Test execute tool with invalid tool name"""
+        execute_tool = create_meta_execute_tool(tools_collection)
+
+        with pytest.raises(ValueError, match="Tool 'invalid_tool' not found"):
+            execute_tool.execute(
+                {
+                    "toolName": "invalid_tool",
+                    "params": {},
+                }
+            )
+
+    def test_execute_tool_call(self, tools_collection):
+        """Test calling the execute tool with call method"""
+        execute_tool = create_meta_execute_tool(tools_collection)
+
+        # Mock the actual tool execution by patching the requests
+        with responses.RequestsMock() as rsps:
+            rsps.add(
+                responses.GET,
+                "https://api.example.com/hris/employee",
+                json={"success": True, "employees": []},
+                status=200,
+            )
+
+            # Call the meta execute tool
+            result = execute_tool.call(toolName="hris_list_employee", params={"limit": 10})
+
+            assert result is not None
+            assert "success" in result or "employees" in result
+
+
+class TestToolsMetaTools:
+    """Test the meta_tools method on Tools collection"""
+
+    def test_meta_tools_creation(self, tools_collection):
+        """Test creating meta tools from a Tools collection"""
+        meta_tools = tools_collection.meta_tools()
+
+        assert isinstance(meta_tools, Tools)
+        assert len(meta_tools) == 2
+
+        # Check tool names
+        tool_names = [tool.name for tool in meta_tools.tools]
+        assert "meta_filter_relevant_tools" in tool_names
+        assert "meta_execute_tool" in tool_names
+
+    def test_meta_tools_functionality(self, tools_collection):
+        """Test that meta tools work correctly"""
+        meta_tools = tools_collection.meta_tools()
+
+        # Get the filter tool
+        filter_tool = meta_tools.get_tool("meta_filter_relevant_tools")
+        assert filter_tool is not None
+
+        # Search for tools
+        result = filter_tool.execute(
+            {
+                "query": "create employee",
+                "limit": 1,
+            }
+        )
+
+        assert "tools" in result
+        assert len(result["tools"]) > 0
+
+        # The top result should be related to creating employees
+        top_tool = result["tools"][0]
+        assert "employee" in top_tool["name"].lower() or "create" in top_tool["name"].lower()
diff --git a/tests/test_tool_calling.py b/tests/test_tool_calling.py
new file mode 100644
index 0000000..3e30e74
--- /dev/null
+++ b/tests/test_tool_calling.py
@@ -0,0 +1,161 @@
+"""Tests for tool calling functionality"""
+
+import json
+
+import pytest
+import responses
+
+from stackone_ai import StackOneTool
+from stackone_ai.models import ExecuteConfig, ToolParameters
+
+
+@pytest.fixture
+def mock_tool():
+    """Create a mock tool for testing"""
+    execute_config = ExecuteConfig(
+        name="test_tool",
+        method="POST",
+        url="https://api.example.com/test",
+        headers={"Content-Type": "application/json"},
+    )
+
+    parameters = ToolParameters(
+        type="object",
+        properties={
+            "name": {"type": "string", "description": "Name parameter"},
+            "value": {"type": "number", "description": "Value parameter"},
+        },
+    )
+
+    tool = StackOneTool(
+        description="Test tool",
+        parameters=parameters,
+        _execute_config=execute_config,
+        _api_key="test_api_key",
+    )
+
+    return tool
+
+
+class TestToolCalling:
+    """Test tool calling functionality"""
+
+    @responses.activate
+    def test_call_with_kwargs(self, mock_tool):
+        """Test calling a tool with keyword arguments"""
+        # Mock the API response
+        responses.add(
+            responses.POST,
+            "https://api.example.com/test",
+            json={"success": True, "result": "test_result"},
+            status=200,
+        )
+
+        # Call the tool with kwargs
+        result = mock_tool.call(name="test", value=42)
+
+        # Verify the result
+        assert result == {"success": True, "result": "test_result"}
+
+        # Verify the request was made correctly
+        assert len(responses.calls) == 1
+        request = responses.calls[0].request
+        assert json.loads(request.body) == {"name": "test", "value": 42}
+
+    @responses.activate
+    def test_call_with_dict_arg(self, mock_tool):
+        """Test calling a tool with a dictionary argument"""
+        # Mock the API response
+        responses.add(
+            responses.POST,
+            "https://api.example.com/test",
+            json={"success": True, "result": "test_result"},
+            status=200,
+        )
+
+        # Call the tool with a dict
+        result = mock_tool.call({"name": "test", "value": 42})
+
+        # Verify the result
+        assert result == {"success": True, "result": "test_result"}
+
+        # Verify the request
+        assert len(responses.calls) == 1
+        request = responses.calls[0].request
+        assert json.loads(request.body) == {"name": "test", "value": 42}
+
+    @responses.activate
+    def test_call_with_json_string(self, mock_tool):
+        """Test calling a tool with a JSON string argument"""
+        # Mock the API response
+        responses.add(
+            responses.POST,
+            "https://api.example.com/test",
+            json={"success": True, "result": "test_result"},
+            status=200,
+        )
+
+        # Call the tool with a JSON string
+        result = mock_tool.call('{"name": "test", "value": 42}')
+
+        # Verify the result
+        assert result == {"success": True, "result": "test_result"}
+
+        # Verify the request
+        assert len(responses.calls) == 1
+        request = responses.calls[0].request
+        assert json.loads(request.body) == {"name": "test", "value": 42}
+
+    def test_call_with_both_args_and_kwargs_raises_error(self, mock_tool):
+        """Test that providing both args and kwargs raises an error"""
+        with pytest.raises(ValueError, match="Cannot provide both positional and keyword arguments"):
+            mock_tool.call({"name": "test"}, value=42)
+
+    def test_call_with_multiple_args_raises_error(self, mock_tool):
+        """Test that providing multiple positional arguments raises an error"""
+        with pytest.raises(ValueError, match="Only one positional argument is allowed"):
+            mock_tool.call({"name": "test"}, {"value": 42})
+
+    @pytest.mark.asyncio
+    @responses.activate
+    async def test_acall_with_kwargs(self, mock_tool):
+        """Test async calling a tool with keyword arguments"""
+        # Mock the API response
+        responses.add(
+            responses.POST,
+            "https://api.example.com/test",
+            json={"success": True, "result": "async_result"},
+            status=200,
+        )
+
+        # Call the tool asynchronously
+        result = await mock_tool.acall(name="test", value=42)
+
+        # Verify the result
+        assert result == {"success": True, "result": "async_result"}
+
+    @responses.activate
+    def test_call_without_arguments(self, mock_tool):
+        """Test calling a tool without any arguments"""
+        # Mock the API response
+        responses.add(
+            responses.POST,
+            "https://api.example.com/test",
+            json={"success": True, "result": "no_args"},
+            status=200,
+        )
+
+        # Call the tool without arguments
+        result = mock_tool.call()
+
+        # Verify the result
+        assert result == {"success": True, "result": "no_args"}
+
+        # Verify the request body is empty or contains empty JSON
+        assert len(responses.calls) == 1
+        request = responses.calls[0].request
+        # Handle case where body might be None for empty POST
+        if request.body:
+            assert json.loads(request.body) == {}
+        else:
+            assert request.body is None or request.body == b""
diff --git a/uv.lock b/uv.lock
index 6d11b0b..3f983ac 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,22 +2,22 @@ version = 1
 revision = 3
 requires-python = ">=3.11"
 resolution-markers = [
-    "python_full_version >= '3.12.4'",
-    "python_full_version < '3.12.4'",
+    "python_full_version >= '3.13'",
+    "python_full_version < '3.13'",
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.6"
+version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
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+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.11.12"
+version = "3.12.15"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -28,68 +28,72 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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 [package.optional-dependencies]
@@ -497,11 +527,10 @@ toml = [
 
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-version = "0.102.0"
+version = "0.157.0"
 source = { registry = "https://pypi.org/simple" }
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-    { name = "auth0-python" },
     { name = "blinker" },
     { name = "chromadb" },
     { name = "click" },
@@ -510,88 +539,95 @@ dependencies = [
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     { name = "jsonref" },
     { name = "litellm" },
+    { name = "onnxruntime" },
     { name = "openai" },
     { name = "openpyxl" },
     { name = "opentelemetry-api" },
     { name = "opentelemetry-exporter-otlp-proto-http" },
     { name = "opentelemetry-sdk" },
     { name = "pdfplumber" },
+    { name = "portalocker" },
     { name = "pydantic" },
+    { name = "pyjwt" },
     { name = "python-dotenv" },
     { name = "pyvis" },
     { name = "regex" },
+    { name = "tokenizers" },
     { name = "tomli" },
     { name = "tomli-w" },
     { name = "uv" },
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 [[package]]
 name = "cryptography"
-version = "44.0.1"
+version = "45.0.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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