@createworker/mcp

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Answer a clarification

clarification_answer

Resolve pending clarification requests from workers by selecting the appropriate option. Provide a note if needed to finalize the task decision.

Instructions

Answer a worker's clarifying question (CLARIFICATION_PENDING) by selecting an option.

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": {
      "taskId": "<taskId>",
      "proposalId": "<proposalId>",
      "selectedOptionKey": "<selectedOptionKey>"
    },
    "name": "clarification_answer"
  }
}

Input Schema

Name	Required	Description
`note`	No
`taskId`	Yes	the task id
`proposalId`	Yes	the proposal id from the task
`selectedOptionKey`	Yes	the chosen option key

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jun 30, 2026

TDQS

B3.2/5.0

Behavior2/5

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

With no annotations, the description carries the full burden for behavioral transparency. It only minimally describes the action ('answering'), but does not disclose potential side effects, authorization requirements, or state changes that occur after answering. This is insufficient for a mutation tool.

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 a single direct sentence with no wasted words. It is front-loaded with the action and resource, making it easy to parse quickly.

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

Completeness2/5

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

Given the tool has 4 parameters and no output schema, the description is minimal. It fails to explain the overall workflow, how options are presented, or any prerequisites. More context about what happens after answering is needed for an agent to use the tool correctly.

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 75% with three parameters having descriptions. The tool description adds only generic context about 'selecting an option', which maps to 'selectedOptionKey'. The description does not add meaning beyond what the schema already provides, so a baseline score of 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 clearly states the action ('Answer') and the resource ('worker's clarifying question') and specifies the context ('CLARIFICATION_PENDING'). It distinguishes from sibling tools like 'approval_decide' by focusing on clarification rather than approval. However, it could be more explicit about the selection mechanism.

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

Usage Guidelines3/5

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

The description implies usage when a CLARIFICATION_PENDING state exists, but it does not provide explicit guidance on when not to use this tool or mention alternatives. An agent would infer the use case from the context, but more explicit instructions would improve decision-making.

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