Statey

Here is what a first session looks like. You don't type any of this. Just describe what you want and the agent picks the right tools.

Returns your collections, record counts, and the calling agent's permissions. On a brand-new workspace, you'll see an empty list and two starter templates available.

spin up a task tracker with title, status (todo / in-progress / done), priority, and assignee

Statey generates a schema from your description, validates the field types, and returns a summary. The collection is ready immediately.

add three tasks: set up CI (high priority, todo), write the readme (medium, todo), deploy to staging (high, in-progress)

All three records land in a single all-or-nothing write. Each gets an id, a version counter, and an attribution entry in the activity log.

show me all high-priority tasks that aren't done yet

Returns a filtered, paginated result with a total count. You can group, sort, or filter by any field, all in plain language.

The complete tool surface for the current release, grouped by what they do. You don't call these directly; your agent selects the right one based on what you ask.

Two tools provide context: one lists what exists in the workspace, the other returns the schema for a single collection.

Returns your collections, record counts, schema versions, active triggers, actors, and the calling agent's permissions.

The full schema for one collection: every field and its description, relations, sample records, and available indexes.

Create collections from a description, and evolve them over time. Additive schema changes apply immediately; breaking changes require confirmation.

A new collection from a plain description or a starter template (Tasks or Tickets). Field descriptions are required; they tell the agent what each field means.

make a customers collection with name, plan, mrr

Evolve a collection. Additive changes (a new optional field, a relaxed enum) apply immediately; breaking changes require explicit confirmation. Returns a diff.

Create, read, query, update, and delete records. Writes are attributed and guarded by optimistic concurrency.

Insert 1–100 records at once, validated all-or-nothing so a bad row never lands half-written.

new ticket: runner retry loop drops deliveries, high priority

Fetch one record by id, optionally resolving one level of its relations (a linked task, a parent customer).

Filter, sort, and paginate (up to 200 per page) with a total count. Grouping by a field returns per-group counts in a single call.

A partial update guarded by optimistic concurrency. If the record changed in the meantime, the call returns the current version instead of overwriting it.

A soft delete. The record drops out of queries but is retained in the activity log.

Retire a whole collection: hidden from listings, writes frozen, reads still work. Nothing is deleted, and it reverses cleanly.

Bring an archived collection back to active: visible again, writes re-enabled.

Part of the phase-2 reactive loop. Every change emits an event; triggers match events and dispatch agents or webhooks.

Watch a collection for create, update, or delete events and dispatch an agent or webhook when they match a filter. Causation is tracked across cascades.

when a ticket comes in, have triage-bot look at it

See what automations are running and turn them off.

Per-agent backlog: pending count, the age of the oldest event, and whether the queue is paused.

Resume a paused queue: replay each event in order, or reconcile the whole backlog against current state in a single pass.

catch up on whatever piled up while the runner was down

The activity log records what changed and why; usage reports consumption against your cap.

Search the immutable log by collection, record, actor, action, or time range, and follow causation chains to see what triggered what.

Current-period consumption, spend, and remaining budget against your spend cap. Readable by agents as well as people.

This is the complete tool surface for the current release. All writes are attributed and recorded in the activity log. Tools marked Soon are part of the phase-2 reactive loop and are not yet available.

An MCP server that provides tools to interact with Powerdrill datasets, enabling smart AI data analysis and insights.

Interact with AskTable SaaS or local deployments to query data sources using natural language.

Run SQL queries on data in Amazon S3 using AWS Athena.

Connect Legacy Databases to AI Agents via Model Context Protocol. Open-source bridge for LLM data analysis.

Manage multiple Fireproof JSON document databases with cloud sync capabilities.

Generect MCP connects your live lead database directly to AI models like OpenAI or Claude without exports or delays. It streams enriched, up-to-date contact data (titles, firmographics, signals) straight into prompts so LLMs can personalize, score, and recommend leads automatically in real time.

An MCP server for interacting with Apache Iceberg catalogs and data lakes.

MCP Persistence: your AI Agent now creates and manages databases on its own

An AI-powered server that generates PyAirbyte pipeline code and instructions using OpenAI and connector documentation.