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hedwigai

by com.hedwigai
OverviewDiscussionsAdmin

List a workbook's sections

list_sections
Read-onlyIdempotent

The sections of a workbook, each as heading (its title), path (the headings above it and its own), value (what is under it: table, quote, list, block — code, a chart or a diagram — or section for prose), level, sheet and line. Omit prompt for every heading. Give prompt — any words, 'everything about the Phase 2 programs', 'the risks and what mitigates them' — and jev weighs every heading against the others, by what each section is about, keeping the ones the words ask for; each kept one carries its probability — its share of jev's choice, so several kept share it. The answer's address names exactly the set listed — the same set always has the same address — and get_sections reads it. address is null when nothing was kept. Reads only; no run starts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sheetNoOne tab's id, to look only in that tab.
promptNoWhich part of the workbook you want, in your own words. Omit to list every heading.
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 already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: `address` is stable ('the same set always has the same address'), `address` is null when nothing is kept, kept sections carry a `probability` share, and 'Reads only; no run starts.' These are meaningful traits not present in the annotations.

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?

No sentence is pure filler, and the field enumeration is front-loaded. But the delivery is one long, comma-spliced run-on that is hard to parse, and the return-field detail is interleaved with the filtering behavior. Readability suffers relative to its information density.

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 carries the burden of describing return shape — and it does, enumerating heading, path, value (with its kinds), level, sheet, line and address. Combined with the annotations, an agent has enough to call and interpret the tool. It could be slightly stronger on how the listed set relates to follow-up calls.

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 and the structured fields already document each parameter. The description still adds value by elaborating the semantics of `prompt` — how jev weighs headings against one another and returns `probability` shares — which goes beyond the schema's one-line gloss. It says nothing extra about sheet/context/llm_model, but those are adequately defined in 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 states a concrete verb+resource (lists a workbook's sections) and enumerates the fields each section carries, so the agent knows what comes back. It also implicitly distinguishes itself from get_sections by noting the returned `address` is what get_sections reads. It stops short of a clean one-line purpose statement because the prose is dense and buries the core action.

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?

It explains the two operating modes clearly — omit `prompt` to list every heading, give `prompt` to have jev weigh headings and keep a relevant subset. That is genuine contextual guidance for the primary parameter. However it never states when to choose this tool over the many other navigation siblings (pick_section, target_section, read_workbook), so tool selection is left to inference.

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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