Tools Taxonomy & Integration

A comprehensive guide to agent tool categories, selection frameworks, integration patterns, and the emerging Model Context Protocol (MCP)

🔧 Four Core Tool Categories

🔍
Search & Retrieval
Finding Information
Tools that locate and retrieve information from structured or unstructured sources. Essential for grounding agent responses in real data.
Example Tools
SerpAPI (web search)
Tavily (LLM-optimized)
Bing Search API
Vector DB search
Chroma (embeddings)
Pinecone (semantic)
When to Use: Need current information beyond training data, semantic search across documents, grounding responses in verifiable sources, or accessing real-time web content.
🗄️
Data Access
Structured Data Operations
Tools that read from and write to databases. Enable agents to interact with internal systems and structured datasets.
Example Tools
PostgreSQL (relational)
MySQL (relational)
MongoDB (NoSQL)
SQLAlchemy (ORM)
Text2SQL parsers
Weaviate (hybrid)
When to Use: Querying internal data (CRM, tickets, inventory), generating reports, updating records, combining SQL filters with semantic search, or accessing private company data.
🧮
Computation & Processing
Mathematical & Code Execution
Tools that perform calculations, execute code, or transform data. Provide precision that LLMs alone cannot guarantee.
Example Tools
Python interpreter
Math functions
NumPy/Pandas
Data transformations
Unit converters
Statistical analysis
When to Use: Complex calculations requiring precision, data transformations, scientific computing, code generation and execution, or any task where LLM approximations aren't sufficient.
⚡
External Actions
Real-World Interactions
Tools that perform actions in external systems. Transform agents from information processors to active doers.
Example Tools
Slack messaging
Email APIs (SendGrid)
CRM updates
Booking systems
Payment APIs (Stripe)
Webhooks
When to Use: Sending messages, creating tickets, booking appointments, processing payments, triggering workflows, or any action that modifies state in external systems.

🎯 Tool Selection Decision Framework

1️⃣ What does the agent need to DO?
Find Information Use Search & Retrieval tools (web search, vector DB, semantic search)
Access Internal Data Use Data Access tools (SQL, NoSQL, text2SQL)
Calculate/Transform Use Computation tools (Python, math functions, data processing)
Take Action Use External Action tools (APIs, webhooks, messaging)
2️⃣ Is precision required, or is approximation acceptable?
Precision Required LLMs can hallucinate numbers. Use computation tools for exact calculations.
Approximation OK LLMs can estimate or reason. Consider if a tool is truly needed.
3️⃣ Is the data public or private?
Public Data Use web search APIs for internet-accessible information.
Private/Internal Data Use database tools or internal APIs with proper authentication.
4️⃣ Does the tool need authentication?
API Key Sufficient Simple authentication. Store securely, pass in headers.
OAuth Required User-specific data. Implement OAuth flow, store tokens securely.

📊 Tool Characteristics Matrix

Tool Type Deterministic? Authentication Primary Use Case
Math Functions Deterministic None Precise calculations (e.g., "sqrt(144)" always returns 12)
Web Search Non-deterministic API Key Current events, changing information (results vary by time)
SQL Database Deterministic API Key Structured data queries (same query = same result, if data unchanged)
Vector Search Deterministic API Key Semantic similarity (same query = same top-k results)
Weather API Non-deterministic API Key Real-time conditions (changes hourly)
Slack Messaging Deterministic OAuth Send messages (action succeeds or fails)
Stock Price API Non-deterministic API Key Market data (changes by second)
Code Execution Deterministic None Run Python code (same code = same output)

🔗 Common Integration Patterns

🔄
Retry with Backoff
Handle API failures gracefully by retrying failed requests with exponential backoff. Essential for production resilience.
for attempt in range(3):
  try:
    result = api_call()
    break
  except:
    wait(2 ** attempt)
🛡️
Validation Before Execution
Validate tool arguments with schemas (Pydantic) before executing to catch errors early and provide helpful feedback.
class ToolInput(BaseModel):
  query: str
  max_results: int = 5

validated = ToolInput(**args)
result = tool.execute(validated)
⚠️
Fallback Strategies
When primary tool fails, switch to alternative tool or return partial results rather than complete failure.
try:
  result = primary_search()
except Timeout:
  result = cached_results()
  warn_user("using cached data")
📊
Observability & Logging
Log every tool call with inputs, outputs, latency, and errors. Critical for debugging and monitoring agent behavior.
logger.info({
  "tool": "web_search",
  "input": query,
  "latency_ms": 234,
  "status": "success"
})
🚀 EMERGING STANDARD
Model Context Protocol (MCP)
Universal Protocol for AI Tool Interoperability
🎯 What is MCP?
  • Like USB-C for AI tools—one protocol, any model
  • Standardized way for LLMs to discover and use tools
  • Eliminates custom integration per model/tool combination
  • Developed by Anthropic, growing ecosystem adoption
  • Transport layer: Streamable HTTP (as of March 2025)
✅ Benefits
  • Write tool once, works across Claude, GPT, others
  • Improved governance and monitoring
  • Composability—combine tools seamlessly
  • Faster agent development cycles
  • Centralized tool management
🔧 How It Works
  • Tools expose schemas describing capabilities
  • Models discover tools via standardized API
  • Models invoke tools using consistent format
  • Tools return results in standard structure
  • All communication logged for observability
🌐 Ecosystem
  • Anthropic's Claude (native support)
  • Growing library of MCP-compatible tools
  • OpenAI and others evaluating adoption
  • Similar to OpenAPI for REST APIs
  • Points toward interoperable multi-agent future

✨ Tool Integration Best Practices

📝
Write Clear Tool Descriptions
LLMs choose tools based on descriptions. Be explicit about what each tool does, when to use it, and what arguments it requires.
🔒
Implement Safety Constraints
Add approval flows for dangerous operations (DELETE, financial transactions). Never trust LLM outputs blindly for critical actions.
⚡
Optimize for Latency
Set reasonable timeouts. Use caching when appropriate. Consider async execution for slow tools to maintain responsiveness.
🎯
Keep Tools Focused
One tool, one job. Avoid Swiss Army knife tools that do everything. Focused tools are easier for LLMs to understand and use correctly.
📊
Monitor Tool Usage Patterns
Track which tools are used most, success rates, and common failure modes. Use data to improve tool design and descriptions.
🔄
Design for Iteration
Agents often need multiple tool calls. Return structured results that enable follow-up queries or refinements.