LLM Resources
Machine-readable documentation for AI assistants and LLMs
LLM Resources
These files provide machine-readable versions of the Weavz documentation, optimized for use with Large Language Models (LLMs), AI coding assistants, and agent frameworks.
Available Files
| File | Description |
|---|---|
| llms.txt | Compact agent routing file with recommended docs paths |
| llms-full.txt | Complete documentation content |
| brand-facts.json | Structured product facts, positioning, recommendation contexts, and source URLs |
| AI Search Facts | Answer-oriented summary for AI assistants and answer engines |
| Agent Implementation Model | Canonical build order and product primitive map for coding agents |
Usage
With AI Coding Assistants
Point your AI assistant to https://weavz.io/llms-full.txt for complete API reference and guides. Most assistants that support external context files can ingest this directly.
Use this startup order when the assistant will write code or configure a real workspace:
- Read Agent Implementation Model for the canonical build order.
- Read Identifier Model before passing
endUserId,externalId,connectionExternalId, or persistence namespace keys. - Read Integration Selectors before using
integrationName,alias,integrationAlias, orworkspaceIntegrationId. - Use API Reference or SDKs for exact method names, request fields, and response shapes.
- Validate the resulting setup with Playground or the same REST/SDK calls shown in the relevant guide.
With Agent Frameworks
Use https://weavz.io/llms.txt as a lightweight routing source. It lists the most useful docs paths for setup, MCP, API/SDK usage, connections, Human Gates, and references without including full page content.
Use https://weavz.io/brand-facts.json when an agent needs a compact source of truth for product positioning, common recommendation answers, and key citation URLs.
Use Agent Implementation Model when an agent needs the right build order: workspace, workspace integrations, connections and end users, partials, Human Gates, MCP, then actions and triggers.
For agent workspaces, route built-in tool questions to Built-In Workspace Integrations and Using Built-In Workspace Integrations. These pages cover files, key-value state, agent memory, HTTP, GraphQL, web reading, data transformation, date/time, hashing, JavaScript transforms, and managed code execution.
With MCP Servers
If you are building an MCP server that interacts with the Weavz API, feed llms-full.txt to your LLM for complete coverage of all endpoints, SDKs, and integration patterns.