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📞
Function Calling
Structured interface for AI to invoke external functions and APIs
Complexity: mediumTool Use
🎯 30-Second Overview
Pattern: Enable AI to invoke external functions and APIs through structured schemas
Why: Breaks LLM knowledge limitations, enables real-time data access, external actions, and precise calculations
Key Insight: Schema-driven function calling with parameter validation, parallel execution, and security controls
⚡ Quick Implementation
1Define Schema:JSON schema with parameters & types
2Register Tools:Add to function registry with auth
3Validate Params:Sanitize & validate all inputs
4Execute:Call API with timeouts & retries
5Process Results:Normalize & integrate responses
Example: get_weather(location="NYC", units="celsius") → {"temp": 18, "condition": "cloudy"}
📋 Do's & Don'ts
✅Write detailed function descriptions (3-4+ sentences minimum)
✅Use strict JSON schemas with required/optional field validation
✅Implement parameter sanitization and injection attack prevention
✅Add timeouts, retries with exponential backoff, and circuit breakers
✅Use parallel execution for independent function calls
✅Cache deterministic function results for cost optimization
❌Use vague function descriptions that lead to hallucinated parameters
❌Allow unbounded parallel calls without rate limiting
❌Retry non-idempotent operations after timeouts
❌Expose raw API credentials or secrets in function calls
❌Skip input validation and security scanning
🚦 When to Use
Use When
- • Need real-time data beyond model training cutoff
- • Require external API integrations or database queries
- • Must perform precise calculations or code execution
- • Triggering actions in external systems (email, scheduling)
Avoid When
- • Information available in model knowledge with acceptable quality
- • Hard real-time requirements where tool overhead breaks SLOs
- • High-risk irreversible actions without human approval
- • Simple tasks solvable with basic text generation
📊 Key Metrics
Function Selection Accuracy
% correct tool chosen from registry
Parameter Extraction Rate
% required params correctly identified
Schema Validation Success
% calls passing validation checks
API Success Rate
% successful external function calls
Parallel Execution Efficiency
Latency reduction vs sequential
Token Usage Optimization
Context tokens per function call
Security Incident Rate
Injection/XSS attempts blocked
Cost per Task
Total cost: model + API + infrastructure
💡 Top Use Cases
Weather & Real-time Data: get_weather("NYC") → current conditions for decision making
Calendar Integration: schedule_meeting(time, attendees) → automated scheduling workflows
Database Operations: query_db(table, filters) → dynamic data retrieval and updates
Code Execution: run_python(code) → computational tasks and data analysis
Email & Communications: send_email(to, subject, body) → automated notifications
Web Search: search_web(query) → real-time information retrieval
File Operations: read_file(path), write_file(path, content) → document processing
API Integrations: call_api(endpoint, params) → external service interactions
References & Further Reading
Deepen your understanding with these curated resources
Foundational Academic Papers
Contribute to this collection
Know a great resource? Submit a pull request to add it.
📞
Function Calling
Structured interface for AI to invoke external functions and APIs
Complexity: mediumTool Use
🎯 30-Second Overview
Pattern: Enable AI to invoke external functions and APIs through structured schemas
Why: Breaks LLM knowledge limitations, enables real-time data access, external actions, and precise calculations
Key Insight: Schema-driven function calling with parameter validation, parallel execution, and security controls
⚡ Quick Implementation
1Define Schema:JSON schema with parameters & types
2Register Tools:Add to function registry with auth
3Validate Params:Sanitize & validate all inputs
4Execute:Call API with timeouts & retries
5Process Results:Normalize & integrate responses
Example: get_weather(location="NYC", units="celsius") → {"temp": 18, "condition": "cloudy"}
📋 Do's & Don'ts
✅Write detailed function descriptions (3-4+ sentences minimum)
✅Use strict JSON schemas with required/optional field validation
✅Implement parameter sanitization and injection attack prevention
✅Add timeouts, retries with exponential backoff, and circuit breakers
✅Use parallel execution for independent function calls
✅Cache deterministic function results for cost optimization
❌Use vague function descriptions that lead to hallucinated parameters
❌Allow unbounded parallel calls without rate limiting
❌Retry non-idempotent operations after timeouts
❌Expose raw API credentials or secrets in function calls
❌Skip input validation and security scanning
🚦 When to Use
Use When
- • Need real-time data beyond model training cutoff
- • Require external API integrations or database queries
- • Must perform precise calculations or code execution
- • Triggering actions in external systems (email, scheduling)
Avoid When
- • Information available in model knowledge with acceptable quality
- • Hard real-time requirements where tool overhead breaks SLOs
- • High-risk irreversible actions without human approval
- • Simple tasks solvable with basic text generation
📊 Key Metrics
Function Selection Accuracy
% correct tool chosen from registry
Parameter Extraction Rate
% required params correctly identified
Schema Validation Success
% calls passing validation checks
API Success Rate
% successful external function calls
Parallel Execution Efficiency
Latency reduction vs sequential
Token Usage Optimization
Context tokens per function call
Security Incident Rate
Injection/XSS attempts blocked
Cost per Task
Total cost: model + API + infrastructure
💡 Top Use Cases
Weather & Real-time Data: get_weather("NYC") → current conditions for decision making
Calendar Integration: schedule_meeting(time, attendees) → automated scheduling workflows
Database Operations: query_db(table, filters) → dynamic data retrieval and updates
Code Execution: run_python(code) → computational tasks and data analysis
Email & Communications: send_email(to, subject, body) → automated notifications
Web Search: search_web(query) → real-time information retrieval
File Operations: read_file(path), write_file(path, content) → document processing
API Integrations: call_api(endpoint, params) → external service interactions
References & Further Reading
Deepen your understanding with these curated resources
Foundational Academic Papers
Contribute to this collection
Know a great resource? Submit a pull request to add it.