Your Calendar as an LLM Tool
Expose your full scheduling infrastructure to any large language model. Schedly's MCP server and REST API make booking creation, availability checking, and calendar management callable tools for every LLM ecosystem.
Large language models are capable of reasoning about complex scheduling problems — multi-constraint availability, team coordination, deadline-aware booking, rescheduling logic — but they need access to real calendar data and booking execution to be useful scheduling assistants. Schedly bridges this gap by exposing your scheduling infrastructure as callable tools through two interfaces: the Model Context Protocol for MCP-native environments, and a REST API for function calling in any LLM framework. Whether you are working with OpenAI's function calling, Anthropic's tool use, Google's function declarations, LangChain tools, LlamaIndex tools, or any other agentic framework, Schedly's scheduling capabilities are one integration away.
The results speak for themselves
Up and running in minutes
No technical setup. No developer required. Fully automated from day one.
Choose your integration pattern: MCP or REST API
For AI host environments (Claude Desktop, Cursor, Windsurf): configure Schedly as an MCP server — the host discovers your scheduling tools automatically. For code-based LLM applications (LangChain, LlamaIndex, custom): define Schedly endpoints as function call tools in your LLM request configuration.
Define the Schedly tool schema for your LLM
Provide the LLM with Schedly's tool definitions: check_availability(event_type_id, date_range, timezone), create_booking(event_type_id, start_time, attendee), cancel_booking(booking_id), reschedule_booking(booking_id, new_start_time). The LLM uses these definitions to understand when and how to call each tool.
The LLM reasons about scheduling requests and calls tools
When a user makes a scheduling request, the LLM reasons about what Schedly tool to call, extracts the parameters from the natural language request, and calls the tool. Schedly executes the operation against your real calendar and returns the result to the LLM.
The LLM presents results and continues the conversation
Schedly returns structured results — available slots, booking confirmations, updated schedules — that the LLM presents naturally in the conversation. The LLM can chain multiple tool calls for complex workflows: check availability, filter for best options, create booking, draft confirmation email.
Everything built in. Nothing bolted on.
Every capability works together as a unified system — not a collection of disconnected features.
OpenAI Function Calling Integration
Define Schedly endpoints as tools in OpenAI API requests using the functions or tools parameter. GPT-4o and GPT-4-turbo automatically identify when to call scheduling tools and extract correct parameters from user messages — enabling natural language scheduling in any OpenAI-powered application.
Anthropic Tool Use Integration
Schedly integrates with Claude's tool use protocol, defining scheduling operations as tools in Anthropic API requests. Claude's tool use accuracy and reasoning quality make it particularly effective for complex multi-step scheduling workflows that require reasoning about multiple constraints.
Google AI Function Declarations
Define Schedly endpoints as function declarations in Gemini API requests. Gemini's function calling supports the full Schedly scheduling tool set, enabling natural language scheduling in Google AI-powered applications and Gemini API integrations.
LangChain and LlamaIndex Tools
Schedly integrates as a LangChain Tool and LlamaIndex Tool, making scheduling available to any agent or chain built on these popular LLM frameworks. Define Schedly as a tool in your agent's tool list and it becomes available to any agent reasoning loop — ReAct, OpenAI Functions, or custom.
Local and Open-Source LLM Support
Schedly's REST API works with any LLM that supports function calling — including local models like Llama 3, Mistral, and Qwen deployed via Ollama, LM Studio, or custom inference servers. Build private AI scheduling assistants that keep all AI inference local while using Schedly for scheduling execution.
Agentic Scheduling Workflows
Schedly's tool interface supports multi-step agentic scheduling workflows where the LLM executes multiple tool calls in sequence: check availability across multiple event types, compare options, identify the best slot based on specified criteria, create the booking, and draft the attendee communication — all within a single agentic loop.
Works for every service business
See how different industries apply AI scheduling to their specific workflows — and the results they get.
Developers building LLM-powered applications add scheduling as a first-class capability via Schedly's tool interface — enabling any user of their application to book meetings through natural conversation
Autonomous AI agents that manage professional workflows can include scheduling operations — booking follow-up calls, rescheduling conflicts, managing recurring meetings — as part of multi-step automated workflows
Developers building personal AI assistants can include Schedly as a scheduling tool — giving the assistant the ability to manage the user's calendar as a native capability alongside email, tasks, and note-taking
AI researchers and educators use Schedly as a real-world scheduling tool in demonstrations of LLM function calling and tool use — showing how LLMs interact with production APIs
Enterprise AI platforms that serve multiple internal use cases can include Schedly as a shared scheduling tool — available to any LLM-powered internal application that needs calendar management capabilities
Before vs. After AI Scheduling
The operational reality that changes the moment you go live.
- Phone tag and email back-and-forth for every booking
- 19%+ no-show rate with no systematic prevention
- Manual reminder calls consuming staff time every morning
- After-hours booking requests sent to voicemail
- Double-bookings from manual calendar management
- No data on booking patterns or no-show causes
- One link replaces all scheduling coordination — forever
- No-show rate drops to under 5% with three-touch reminders
- AI sends every reminder automatically — zero staff time
- 24/7 booking captures every after-hours lead instantly
- Zero double-bookings with real-time calendar intelligence
- Full analytics dashboard showing every booking pattern
Frequently asked questions
Schedly works with all major LLM frameworks: OpenAI's function calling and GPT Actions, Anthropic's tool use, Google's function declarations, LangChain tools, LlamaIndex tools, Autogen, CrewAI, and any other agentic framework that supports external tool calls via REST API or MCP. Local model frameworks like Ollama with function-calling-capable models also work with Schedly's REST API.
Yes. Any local LLM that supports function calling or tool use can call Schedly's REST API for scheduling operations. Deploy Llama 3, Mistral, or Qwen locally via Ollama or LM Studio, define Schedly endpoints as tools in your application, and your local AI assistant gains real scheduling capabilities.
Define the Schedly scheduling operations as tool schemas in your LLM request: tool name, description, and parameter schema for each operation (check_availability, create_booking, cancel_booking, etc.). Provide your Schedly API key for authentication in your server-side API calls. Detailed tool definitions for each major LLM framework are available in Schedly's developer documentation.
Schedly's API responds to availability queries typically in under 200ms and booking creation requests in under 500ms from any major cloud region. This latency is appropriate for real-time LLM tool call chains where the model waits for tool results before continuing its response.
The LLM Tool Use Revolution and What It Means for Scheduling Infrastructure
Tool use is the capability that transforms large language models from sophisticated text processors into agents capable of taking actions in the world. Without tool access, an LLM can discuss scheduling, suggest meeting times, and draft calendar invites — but it cannot actually check your real availability or create a confirmed booking. Tool use changes this: the model can now call external APIs, receive real data, and take real actions. Scheduling is one of the highest-value tool use domains because it has clear inputs (who, what, when), requires real-world state (actual calendar availability), and produces a concrete outcome (a confirmed booking with all downstream workflows). The combination of LLM reasoning and Schedly's scheduling execution creates a qualitatively different capability than either component alone.
Designing Schedly as an Effective LLM Tool
Effective LLM tool design requires balancing granularity and abstraction: too granular (a separate tool for every scheduling operation with low-level parameters) creates complex tool selection problems for the model. Too abstract (a single 'do scheduling' tool with a natural language prompt) loses the precision that structured tool calls provide. Schedly's tool design strikes the right balance: four to six clear operations (check_availability, create_booking, list_bookings, cancel_booking, reschedule_booking) with well-typed parameters that the model can extract from natural language. This design gives the model clear tool selection choices, predictable parameters to populate, and structured outputs to reason about — resulting in high accuracy for real-world scheduling requests across all major LLM providers.
The Convergence of AI Interfaces on Common Scheduling Infrastructure
One of the most significant architectural implications of Schedly's LLM tool integration is that the same scheduling infrastructure — the same availability rules, booking logic, calendar sync, and confirmation workflows — serves multiple AI interfaces simultaneously. Your Schedly account powers the web booking page your clients use, the Claude Desktop MCP integration your executive assistant uses, the Cursor IDE integration your engineering team uses, and the custom LLM application you are building — all from the same configuration, all creating identical booking objects, all triggering the same downstream workflows. This convergence means that every AI interface improvement you enable compounds across all users of your scheduling infrastructure, not just one. Organizations that build on Schedly's API are not choosing a scheduling tool — they are choosing a scheduling infrastructure platform that will serve every AI interface their team and clients use.
Give your LLM real scheduling superpowers — connect Schedly to your AI application today.
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