AI Agents

Worksome provides multiple ways for AI agents and LLMs to interact with the platform — from structured API tools to machine-readable documentation that can be loaded directly into context windows.

Machine-readable documentation

We publish two documentation files following the emerging llms.txt convention for AI-friendly documentation:

llms.txt — Overview

URL: https://docs.worksome.com/llms.txt

A concise summary of the Worksome API: capabilities, entity model, available queries/mutations, webhook events, and links to all documentation pages. Ideal for giving an AI agent a quick overview of what the API can do.

Use this when: Your agent needs to understand the API’s capabilities and decide which documentation to read next.

llms-full.txt — Full documentation

URL: https://docs.worksome.com/llms-full.txt

The complete text of all documentation pages concatenated into a single file — guides, webhook references, integration docs, and more. This file is large but fits within the context window of most modern LLMs.

Every documentation build regenerates both files, so they track changes to the GraphQL schema and the docs. The Last updated line at the top of each file records the build date.

Use this when: Your agent needs to answer detailed questions about the API, write integration code, or understand specific workflows end to end.

Example: loading docs into an AI agent

import httpx

# Load the full documentation into your agent's context
docs = httpx.get("https://docs.worksome.com/llms-full.txt").text

# Use it as context for your LLM
messages = [
    {"role": "system", "content": f"You are a Worksome API assistant.\n\n{docs}"},
    {"role": "user", "content": "How do I create a hire and wait for contract acceptance?"},
]

// Load the overview for quick capability checks
const overview = await fetch('https://docs.worksome.com/llms.txt').then(r => r.text());

// Or the full docs for detailed answers
const fullDocs = await fetch('https://docs.worksome.com/llms-full.txt').then(r => r.text());

Structured API access for agents

For AI agents that need to read and write Worksome data (not just answer questions), use one of these tools:

MCP Server

The MCP Server implements the Model Context Protocol, a standard for connecting AI assistants to external tools. It is in early access; request access from the MCP Server page to get the endpoint and the operations available to you.

Best for: AI assistants like Claude, ChatGPT, or custom agents that support MCP.

CLI with JSON output

The CLI provides broad API coverage (170+ operations) with a --output json flag that outputs structured JSON — ideal for AI agents that can invoke shell commands.

# An AI agent can run CLI commands and parse the JSON output
worksome hires list --active-status ACTIVE --output json
worksome contracts get Q29udHJhY3Q6MTIzNA== --output json

Best for: AI agents that can execute shell commands, or automation pipelines where the CLI is already available.

Choosing the right approach

Other integrations

• GraphQL API — Full programmatic access for custom integrations.
• Webhooks — Real-time event notifications.
• PHP SDK — Official PHP SDK for the Worksome API.
• CLI — Command-line interface with broad API coverage.
• Zapier — No-code automation with triggers and actions.
• MCP Server — AI agent integration via Model Context Protocol.