Skip to main content
Glama

ai-complete_work_item

ai-complete_work_item

Marks a work item as completed with optional result and output. Requires item in 'processing' status — claim first or use atomic claim-and-complete.

Instructions

Marks a work item as completed with optional result and output. If output_item_type was configured, creates the output item. Triggers connected sentry if configured. Item must be in 'processing' status — call ai-claim_work_item first, or use ai-claim_and_complete_work_item to do both atomically.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
output_contentNoContent for output item (if output_item_type was configured)
output_nameNoName for output item (overrides configured name)
resultNoProcessing result (string, object, or any structured data)
work_item_idYesWork item ID (@rid format)
Behavior3/5

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

No annotations are provided, so the description must fully disclose behavior. It mentions marking complete, optional output creation, and sentry triggering, but does not discuss irreversibility, permission requirements, or what happens to the item post-completion.

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?

Three sentences with no redundancy: first states main action, second details optional effects, third provides prerequisite and alternatives. Every sentence adds value.

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?

Given no annotations or output schema, the description covers the main behavior, prerequisites, and alternatives. It lacks explicit mention of return values, but is otherwise complete for understanding tool usage.

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 the baseline is 3. The description adds context linking output_content and output_name to the output creation behavior and mentions overriding configured name, but does not significantly enhance meaning beyond 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?

The description clearly states the verb 'marks' and resource 'work item as completed', and distinguishes from siblings by explicitly mentioning ai-claim_work_item and ai-claim_and_complete_work_item as alternatives.

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?

The description provides explicit context: item must be in 'processing' status, suggests using ai-claim_work_item first or the atomic alternative. It lacks explicit 'when not to use' guidance but covers when and how to use effectively.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mstang/casemgr-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server