Skip to main content
Glama

delimit_agent_complete

Close a dispatched agent task by recording the result summary and files changed, changing task status from dispatched to completed.

Instructions

Close a dispatched agent task by recording the outcome (Pro).

When to use: at the end of an engineering subagent's work, to record the result summary and the files touched on the dispatch record. This is the closing step of the dispatch lifecycle (delimit_agent_dispatch -> [subagent runs] -> this). Without calling this, the task remains "dispatched" in the ledger and dashboards will count it as in-flight. When NOT to use: to hand off ownership to a different model (use delimit_agent_handoff), to dispatch a fresh task (delimit_agent_dispatch), or to read task status without closing (delimit_agent_status). Also: do not call repeatedly on the same task_id — the backend treats a second complete as an error.

Sibling contrast: delimit_agent_handoff transfers active ownership to another model (task stays open); this closes ownership entirely. delimit_agent_status is the read-only sibling.

Side effects: writes a completion record via ai.agent_dispatch.complete_task — the task's status flips from "dispatched" to "completed", result and files_changed are persisted, and an audit log entry is appended. files_changed is coerced from a comma string to a list. No license gate at this level (handled by the backend). No notification — pair with delimit_notify if the operator needs to be told.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYesTask id from delimit_agent_dispatch. Required.
resultNoSummary of what was done.
files_changedNoComma-separated paths of modified files.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations, but description fully details side effects: status change, persistence, audit log, file coercion, no license gate, no notification.

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?

Well-structured with clear headings, front-loaded purpose, and each sentence adds value without redundancy.

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

Completeness5/5

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

Covers lifecycle context, side effects, and behavior. Output schema exists so return format is not required in description.

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. Description adds minor context (coercion of files_changed) but no significant enhancement beyond 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?

Verb 'close' and resource 'dispatched agent task' are explicit. Distinguishes from siblings (handoff, dispatch, status) clearly.

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?

Explicit 'When to use' and 'When NOT to use' sections with specific alternative tool names and warnings against repeated calls.

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/delimit-ai/delimit-mcp-server'

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