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Dart AI task management MCP with batch operations, DartQL selectors, CSV import, zero context rot
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Dart AI task management MCP with batch operations, DartQL selectors, CSV import, zero context rot
Security Report
Valid MCP server (1 strong, 1 medium validity signals). 2 known CVEs in dependencies (0 critical, 2 high severity) Package registry verified. Imported from the Official MCP Registry. Trust signals: 4 highly-trusted packages.
4 files analyzed · 3 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
What You'll Need
Set these up before or after installing:
Environment variable: DART_TOKEN
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-standardbeagle-dart-query": {
"env": {
"DART_TOKEN": "your-dart-token-here"
},
"args": [
"-y",
"@standardbeagle/dart-query"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
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-92 WHERE clause syntax 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 'Task%' -- starts with
title LIKE '%auth%' -- contains substring
Operators: =, !=, <>, >, >=, <, <=, LIKE, IN, NOT IN, BETWEEN, IS NULL, IS NOT NULL, CONTAINS
Aliases: INCLUDES/HAS → CONTAINS · <> → !=
LIKE wildcards: % = any characters, _ = single character (case-insensitive)
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
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