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Answer what a workbook is asking

answer_question

A workbook can be asking its reader something, shown as asking when it is read, one question at a time: whether to remove addresses a mail server refused, whether to remove citations to pages that could not be fetched, or whether to find sources for a document that cites none. yes records the decision and starts the run that does it; no records that the document stays as it is. Either way the answer is kept with the workbook, so nobody and nothing is asked the same question twice. yes starts a run that is billed to this account.

Input Schema

NameRequiredDescriptionDefault
answerYesyes or no.
contextNoExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
workbookYesThe workbook id.
llm_modelNoThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

With annotations present the bar is lower, yet the description still adds material context: `yes` launches a billable run charged to this account, and the answer is persisted so the prompt never repeats. This is real value beyond `readOnlyHint:false` and `openWorldHint:true`. It stops short of saying what happens on a failed run or whether progress must be monitored.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The paragraph is front-loaded with the core condition and consequence, and each sentence carries information. The persistence sentence ('nobody and nothing is asked the same question twice') is slightly padded but still justifies its place by explaining why re-answering is unnecessary.

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?

For a mutating, billable, open-world tool this covers trigger, semantics, cost, and persistence, and no output schema exists so return values need no explanation. Minor gaps remain around post-`yes` monitoring and failure behavior, which the agent must discover from siblings like `list_runs`.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description goes further by explaining the operational meaning of each enum value — `yes` records and starts a run, `no` leaves the document untouched. The `workbook` and analytics-only parameters are left to the schema.

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 names a specific action (answer the pending question a workbook is 'asking') and concretely illustrates the three question types (refused mail addresses, unfetchable citations, missing sources), so the agent knows exactly what operation this performs. It does not explicitly contrast with the sibling `ask` tool, leaving the ask/answer distinction to be inferred.

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 trigger condition is stated plainly: use this when a workbook is read and shows `asking`, one question at a time. The yes/no consequence is spelled out (`yes` records the decision and starts the run, `no` records that the document stays as-is). No sibling alternatives such as `stop_run` or `read_workbook` are named for adjacent situations.

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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