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alterlab_task_status
Read-onlyIdempotent

Check an open or finished task: its status, failure reason, pages and actions used, expiry and any pending question or approval. Each pending request carries an elicitation block (message and answer schema) to show the user. Poll it while waiting for the user, then answer with alterlab_task_answer.

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": {
      "task_id": "<task_id>"
    },
    "name": "alterlab_task_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
task_idYesTask id from alterlab_task_start

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: pending requests carry an elicitation block with a message and answer schema intended for display to the user, and polling is the expected interaction loop. It omits polling cadence or rate-limit guidance, which would have pushed this to a 5.

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, both dense with information, and the returned-field enumeration is front-loaded ahead of the interaction instruction. No filler or redundant restatement of the title.

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?

There is no output schema, so the description must convey what comes back — it does so by listing status, failure reason, pages, actions, expiry, and the elicitation block shape. For a single-parameter read tool this is nearly complete; only polling mechanics (frequency, backoff) are left unstated.

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% and the single task_id parameter is already documented as coming from alterlab_task_start, so the schema does the heavy lifting. The description's mention of task context adds framing but no new parameter-level semantics. Baseline 3 is appropriate.

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

Purpose4/5

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

The description gives a specific verb (check) and resource (task) and enumerates the returned facets: status, failure reason, pages and actions used, expiry, pending question or approval. That is far beyond a name restatement and lets an agent distinguish this from action-oriented siblings like alterlab_task_act or alterlab_task_answer. It stops short of an explicit contrast with alterlab_task_observe, which is the closest-looking sibling.

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?

It states a concrete usage pattern — poll while waiting for the user — and routes the follow-up action to a named alternative, alterlab_task_answer. There is no explicit when-not-to-use clause (e.g., use task_observe for live page inspection), but the operational context is clear enough for correct selection.

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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