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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. Today there is one question: sources under the current version have moved, and someone has to decide whether to write it again. yes records the decision and starts a run; no records that the document was looked at and judged still good. 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

TableJSON 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

A3.7/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false, idempotentHint=false, openWorldHint=true and destructiveHint=false; the description adds real value beyond these by disclosing that 'yes' starts a run billed to the account and that any answer is persisted so the question is not asked again. These are meaningful side effects (billing, run creation, state change) that the annotations alone do not convey.

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

Conciseness3/5

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

The content is informative and each sentence carries some payload, but it reads as a conceptual narrative rather than a front-loaded definition, and the core 'this tool records a workbook's answer' statement arrives only after the framing about what a workbook 'is asking.' It is reasonably sized but not tightly structured.

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 carry behavioral weight, and it does explain the yes/no outcomes, persistence, and billing side effect. It stops short of describing the run that 'yes' starts or any permission requirements, but for a two-decision tool with 100% schema coverage and clear annotations this is largely complete.

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 adds substantive meaning to the enum by spelling out what 'yes' and 'no' each cause (record decision + start billed run vs. record that the document was judged still good). The 'context' and 'llm_model' parameters are already fully documented in the schema, so nothing is missing.

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 establishes a specific verb+resource: recording an answer to a question a workbook is 'asking,' with the concrete current case (sources moved under the current version). An agent can distinguish this from recheck_sources or save_sources, though it never names those siblings explicitly. The action itself is described narratively rather than front-loaded, which slightly blunts clarity.

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 trigger is implied ('shown as asking when it is read'), and the yes/no branches are explained, giving usable context for when a call is warranted. However, there is no explicit when-not guidance and no named alternatives (e.g. recheck_sources, save_sources) to route between. Usage is therefore inferable but not directive.

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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