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dart-query

dart-query: Zero Context Rot Task Management

Update hundreds of Dart AI tasks in a single MCP call. Zero context rot. ~200 tokens instead of ~30K.
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Why dart-query?

Section titled “Why dart-query?”

Traditional MCP task tools loop through tasks one-by-one, filling your context window with intermediate JSON. A 50-task status update consumes ~30,000 tokens and degrades every subsequent response.

dart-query uses DartQL selectors and server-side batch operations to do the same update in a single call for ~200 tokens. Your context stays clean. Your agent stays sharp.

Key Features

Section titled “Key Features”

DartQL Selectors

SQL-92 WHERE clause syntax for targeting tasks. Filter by dartboard, status, priority, tags, dates, and more with operators like LIKE, IN, BETWEEN, and CONTAINS.

Batch Operations

Update or delete hundreds of tasks in a single call. No loops, no intermediate JSON, no context window bloat.

CSV Import

Bulk-create tasks from CSV files with automatic field mapping and a validation phase that catches errors before creating anything.

Dry-Run Safety

Preview every batch operation before executing. All batch updates and deletes support dry_run: true so you see exactly what will change.

Progressive Discovery

Start with info() to see available tools and workspace structure. Use get_config() to discover dartboards, statuses, and priorities. No guessing.

Full Task Lifecycle

Create, read, update, delete, comment, search, and manage relationships. Single-task precision when you need it, batch power when you don’t.

Quick Example

Section titled “Quick Example”
// Find and update all high-priority Engineering bugs to "Doing"
execute_dartql({
  query: "UPDATE WHERE dartboard = 'Engineering' AND priority = 4 AND tags CONTAINS 'bug' SET status = 'Doing'",
  dry_run: true   // preview first
})

One call. ~200 tokens. Zero context rot.