> ## Documentation Index
> Fetch the complete documentation index at: https://docs.weavable.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Plug real work context into your entire AI stack

<iframe width="100%" height="420" src="https://www.youtube.com/embed/oKiyjAzTfU8" title="Weavable" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen />

Weavable is the context layer between your stack and your agents — structured, scoped, and maintained — giving AI persistent access to your team's work across Jira, GitHub, Slack, Linear, Notion, HubSpot, Zendesk, and 25+ other tools through a single MCP connection.

Ask in a conversation:

> *Summarize what the team shipped this week and draft a status email for leadership.*

Or let an agent act on it:

> *Pull the latest customer feedback trends and draft a product brief.*

## Why Weavable

### One connection, all your tools

Skip setting up and maintaining individual MCP servers for every tool. One Weavable connection — one endpoint, one auth — gives AI access to 25+ tools at once. For agents, that's one reliable data source instead of a dozen fragile integrations.

### A connected graph, not disconnected fragments

Individual tool MCPs return siloed data. Weavable **contexts** combine multiple tools into a unified view, mapping relationships across your stack before any query runs. The model reasons over a connected graph, not five separate feeds to stitch together.

### Built on a changelog, not a snapshot

Weavable runs a continuous changelog across your stack — what changed, when, and what it connects to, tracked *before* you ask. So *"what's happening with Acme"* is built on accumulated changes across HubSpot, Jira, and Slack, not a snapshot taken a second ago.

### Deterministic pipeline, not raw API data

A deterministic pipeline scopes, ranks, and de-noises your data before any AI gets involved. Your model receives structured, ready-to-reason-about context — not thousands of lines of raw JSON. Same scoped foundation every run: same answer at 9am and at midnight.

### Portable across any AI

Set up your contexts once. Use them from Claude, ChatGPT, Cursor, Windsurf, custom agents, or anything else that supports MCP.

## Get connected

You'll need a Weavable account with at least one [context](/product/contexts) set up, and an AI assistant or agent that supports MCP.

The MCP server is at:

```
https://mcp.weavable.ai/mcp
```

See [Connect a client](/mcp/connect) for step-by-step setup for Claude, Claude Code, ChatGPT, VS Code, and other clients.

## Example use cases

* **Engineering manager** — *"Pull what the team shipped and what's blocked since yesterday, and give me three talking points for standup."*
* **Product manager** — *"Draft a weekly status email covering what shipped, what's in flight, and any risks."*
* **Sales lead** — *"Summarize which deals moved stage in HubSpot this week and draft follow-up notes for each."*
* **Support lead** — *"Identify the top themes in Zendesk tickets from the past two weeks and draft a summary for the product team."*
* **Design lead** — *"Pull Figma updates and Slack feedback since our last review and compile them into a changelog."*
* **Agents** — daily briefings, automated reports from this month's customer feedback, hourly P0 monitoring — no human in the loop.