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.
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 set up, and an AI assistant or agent that supports MCP. The MCP server is at: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.