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hedwigai

by com.hedwigai
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List workbooks

list_workbooks
Read-onlyIdempotent

Every workbook this account has, with its title, the prompt of its latest run (asked), the folder it sits in, its version, when it last changed, and its state. state is one of: editing (a run is writing it now), needs_input (the document stopped on a question for a person), stale (a source it cites has changed since it was written), fresh (changed since you last looked), idle. Each workbook's id is what the other tools take as workbook.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
folderNoOnly workbooks in this folder and below it. Omit for all of them.
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."
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 already carry the read-only/idempotent/non-destructive profile, so the bar is lower, yet the description adds real value by defining each `state` value (editing, needs_input, stale, fresh, idle) and noting the id contract with other tools. It goes beyond the structured fields rather than restating them.

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?

It is dense but front-loaded with the returned fields, and every sentence (field list, state definitions, id contract) earns its place. The state enumeration is slightly list-heavy but stays within a reasonable size.

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?

With no output schema, the description correctly takes on the burden of describing return values (title, asked, folder, version, last-changed, state) and does so thoroughly. Only the analytics/pagination behavior is left unspecified, a minor gap.

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 the schema already documents all three params. The description adds no syntax or semantics for `folder` (e.g. recursive behavior) or the context/llm_model analytics params, so baseline 3 applies.

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 states a specific verb+resource ('every workbook this account has') and enumerates the returned fields, so the scope is unambiguous. It implicitly contrasts with search/find siblings by saying 'every workbook,' but never names them to differentiate explicitly.

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?

Usage is implied ('Every workbook this account has' suggests a bulk listing) and the final sentence explains that ids feed other tools, which hints at when to reach for it. However there is no explicit when-to-use vs alternatives (e.g. search_workbooks, find_workbook) or any exclusion condition.

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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