About dart-query
dart-query is an MCP server for managing Dart AI tasks and documents. Its DartQL selectors and server-side batch operations target many tasks in one call, with dry runs and CSV validation for safer bulk changes.
Key features
- 01DartQL task selectors
- 02Batch update and delete
- 03Single-task CRUD and search
- 04CSV import with validation
- 05Document management
- 06Dry runs and delete confirmation
Use cases
- 01Update tasks matching a selector
- 02Preview bulk changes before execution
- 03Create tasks from validated CSV
- 04Find and manage Dart AI documents
MCP support
Exposes Dart AI task and document operations to MCP clients, including DartQL-based batch tools, discovery, and workspace configuration.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
npx -y @standardbeagle/dart-queryDART_TOKENSecretYour Dart AI API token (get it from: https://app.dartai.com/?settings=account)Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
24Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| level | enum | No | Detail level: overview=categories, group=tools in category, tool=full documentationoverview | group | tool |
| target | string | No | Group name (when level=group) or tool name (when level=tool) |
Details
From the project READMEdart-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
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.