io.github.standardbeagle/dart-query
Dart AI task management MCP with batch operations, DartQL selectors, CSV import, zero context rot
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dart-query
MCP server for Dart AI task management, optimized for batch operations and minimal context usage.
Instead of looping through tasks one-by-one (filling your context window with intermediate JSON), dart-query uses DartQL selectors and server-side batch operations to update hundreds of tasks in a single call. A 50-task update that would normally consume ~30K tokens takes ~200 tokens with zero context rot.
Quick Start
1. Get Your Dart AI Token
Visit https://app.dartai.com/?settings=account and copy your token (starts with dsa_).
2. Configure MCP
npx (recommended)
{
"mcpServers": {
"dart-query": {
"command": "npx",
"args": ["-y", "@standardbeagle/dart-query"],
"env": {
"DART_TOKEN": "dsa_your_token_here"
}
}
}
}
SLOP-MCP (v0.10.0+)
slop register dart-query \
--command npx \
--args "-y" "@standardbeagle/dart-query" \
--env DART_TOKEN=dsa_your_token_here \
--scope user
3. Verify
info({ level: "overview" })
4. Example: Batch Update
// Preview first
batch_update_tasks({
selector: "dartboard = 'Engineering' AND priority = 'high'",
updates: { status: "Doing" },
dry_run: true
})
// Execute
batch_update_tasks({
selector: "dartboard = 'Engineering' AND priority = 'high'",
updates: { status: "Doing" },
dry_run: false
})
Tools
| Group | Tools | Purpose |
|---|---|---|
| Discovery | info, get_config | Explore capabilities, workspace config |
| Task CRUD | create_task, get_task, update_task, delete_task, add_task_comment | Single task operations |
| Query | list_tasks, search_tasks | Find tasks with filters or full-text search |
| Batch | batch_update_tasks, batch_delete_tasks, get_batch_status | Bulk operations with DartQL selectors |
| Import | import_tasks_csv | Bulk create from CSV with validation |
| Docs | list_docs, create_doc, get_doc, update_doc, delete_doc | Document management |
See TOOLS.md for full parameter references, DartQL syntax, and CSV import format.
DartQL Selectors
SQL-like WHERE clauses for targeting tasks in batch operations:
dartboard = 'Engineering' AND priority = 'high' AND tags CONTAINS 'bug'
due_at < '2026-01-18' AND status != 'Done'
title LIKE '%auth%'
Safety
All Dart AI operations are production (no sandbox). dart-query provides:
- Dry-run mode on all batch operations — preview before executing
- Validation phase for CSV imports — catch errors before creating anything
- Confirmation flag (
confirm: true) required for batch deletes - Recoverable deletes — tasks move to trash, not permanent deletion
License
MIT
