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aiworkhub_manager_recipe_run_status

Read-only

Check the status of a detached recipe run, optionally wait for it to finish, and retrieve its state, bounded digest, and child log path when completed.

Instructions

MANAGER READ: the state of one detached recipe run, optionally waiting for it to finish.

Returns the run's state (queued, waiting_memory, running, completed, failed, refused or lost). A completed run carries the same bounded digest the synchronous aiworkhub_manager_recipe_run returns, plus the child log path. A run whose process died before finishing reads as lost.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "run_id": "<run_id>"
    },
    "name": "aiworkhub_manager_recipe_run_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
run_idYesThe 32-character lowercase hex run_id returned by aiworkhub_manager_recipe_run with detach=true.
wait_secondsNoSeconds to wait for a terminal state before returning the current one; clamped to 0-300.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.12.80

TDQS

A4.5/5.0
Behavior5/5

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

Even with readOnlyHint and openWorldHint annotations, the description adds substantial behavioral context: the full set of possible states, the wait semantics, the completed-run digest behavior, the child log path, and the meaning of a lost run.

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?

The description is front-loaded with the core operation and uses the remaining sentences to explain outcomes and edge cases. Every sentence adds useful information without repetition.

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?

With annotations covering read-only safety, an output schema, and high parameter coverage, the description supplies the remaining operational context an agent needs: state values, wait behavior, digest behavior, and lost-run semantics.

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 description coverage is 100%, so both parameters are already documented in the schema. The description reinforces the wait behavior and the detached-run origin of run_id, but it does not add syntax or format details 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 states a specific verb and resource: retrieving the state of one detached recipe run. It also contrasts this with the synchronous aiworkhub_manager_recipe_run, so the agent can distinguish it from the related runner tool.

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 makes the usage context clear by specifying a detached recipe run and an optional wait. It does not explicitly say when not to use it or name alternatives beyond the synchronous run tool, so it falls short of full when/when-not guidance.

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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