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by com.letsweft
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Bulk Create Tasks (Import)

bulk_create_tasks

Use this when the user wants to import many tasks at once — a CSV/JSON export from Linear, Jira, Notion or Trello, or any list of 3+ tasks from the conversation. Creates the whole batch in ONE call and one database transaction (all-or-nothing) instead of N create_task calls, so it is far cheaper on the monthly AI-call quota. Address columns by semantic ('backlog'/'todo'/'doing'/'done') or name; if the export references cycles/iterations, create the sprints first (create_sprint) and pass their sprintId per task. Tasks land in each column in array order, after the column's existing tasks.

Input Schema

NameRequiredDescriptionDefault
tasksYesTasks to create, in order

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / tasks / items / properties / dueAt / description
      Added value: +"The day this is due, as an ISO date-time (only the date part is used, e.g. \"2026-10-31T00:00:00Z\"). Put a date on anything the user said has a deadline or a day to check back — dated tasks show on the user's Weft calendar and in their subscribed Google/Apple/Outlook calendar. Pass null to clear."
    • addedInput schema / properties / tasks / items / properties / startAt
      Added value: +{
+  "anyOf": [
+    {
+      "format": "date-time",
+      "pattern": "^(?:(?:\\d\\d[2468][048]|\\d\\d[13579][26]|\\d\\d0[48]|[02468][048]00|[13579][26]00)-02-29|\\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\\d|30)|(?:02)-(?:0[1-9]|1\\d|2[0-8])))T(?:(?:[01]\\d|2[0-3]):[0-5]\\d(?::[0-5]\\d(?:\\.\\d+)?)?(?:Z|([+-](?:[01]\\d|2[0-3]):[0-5]\\d)))$",
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "description": "The day work begins, same format as dueAt. With dueAt it makes a span on the calendar (\"measure 5–12 November\"); never after dueAt. Pass null to clear."
+}
  2. Changed1 schema field changed
    • removedInput schema / $schema
      Removed value: -"https://json-schema.org/draft/2020-12/schema"
  3. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  4. First observed

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only say it is a non-read-only, non-destructive, closed-world write. The description adds genuinely new behavioral facts: single database transaction with all-or-nothing semantics, cheaper on the monthly AI-call quota, and deterministic ordering (tasks land in each column in array order, after existing tasks). These go well beyond what the annotations disclose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the trigger condition, then atomicity, then the quota rationale, then the column/sprint mechanics. Dense but purposeful; the parenthetical asides are the only mild padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Only one top-level parameter with full schema coverage and no output schema, so little needs explaining. The description covers atomicity, ordering, quota cost, and sprint prerequisites, but says nothing about what the batch returns (IDs created) or how partial-input validation errors surface despite the all-or-nothing claim.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the 3 baseline is already met by structured fields. The description still adds meaning for two key fields: 'column' accepts either a semantic value or a column name, and 'sprintId' must come from a sprint created beforehand. No guidance on dueAt/startAt/assignee beyond what the schema already documents.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('import many tasks at once', 'creates the whole batch') and explicitly contrasts with the sibling create_task ('instead of N create_task calls'). An agent can distinguish this from create_task without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete trigger conditions (CSV/JSON export from Linear, Jira, Notion, Trello, or any list of 3+ tasks from the conversation) and names the alternative (N create_task calls). It also supplies a prerequisite workflow: create sprints with create_sprint first if the export references cycles/iterations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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