Skip to main content
Glama

start_task

Starts an AI task by moving it to In Progress, setting AI status to PROCESSING, and returning full context including task, comments, GitHub info, and columns. Accepts task ID or number.

Instructions

Start working on an AI task. Moves task to In Progress, sets AI status to PROCESSING, returns full context (task, comments, GitHub info, columns). Accepts taskId OR taskNumber. Call this FIRST for every AI task.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskIdNoTask ID (provide this OR taskNumber)
teamIdNoTeam ID (auto from session if omitted)
projectIdNoProject ID (auto from session if omitted)
taskNumberNoHuman-readable task number, e.g. 43

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4/5.0
Behavior3/5

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

No annotations, so the description must disclose behavior. It discloses the status changes and return context, but doesn't mention edge cases like idempotency, prerequisites, or permission requirements. Transparent about the core effect but not comprehensive.

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

Conciseness5/5

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

Two sentences, no fluff, main action and key detail (OR condition) front-loaded. Efficient and well-structured.

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?

No output schema, so description must explain return, which it does ('full context'). It also gives usage context ('FIRST'). Lacks error handling and prerequisites, but schema covers optional parameters. Adequate for a mutation tool.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. The description reinforces the OR condition between taskId and taskNumber, which is already in the schema, so it adds little new information. teamId/projectId are left to the schema.

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 action ('Start working on an AI task') with clear effects (moves to In Progress, sets AI status to PROCESSING) and return (full context). The 'FIRST' directive differentiates it from sibling tools like move_task and batch_start_tasks.

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

Usage Guidelines4/5

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

Explicitly instructs 'Call this FIRST for every AI task', which is strong usage guidance. However, it doesn't mention when not to use it or alternative tools like batch_start_tasks for multiple tasks.

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