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hedwigai

by com.hedwigai
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Pick a section out of a workbook

pick_section
Read-onlyIdempotent

One section of a workbook — the prose under a heading — chosen from your own words. Say what it is about in prompt — 'ATH434 in multiple system atrophy', 'the competitive landscape' — and the section that is about it is chosen from every heading in the workbook, as the document has them; you do not need to know a heading. The answer's section holds heading (the headings above it and its own), level, line and text (the section as written, subsections included), with confidence; considered is how likely the closest others were. section is null when none is what you asked for. Omit prompt to list the headings instead. Reads only; no run starts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sheetNoOne tab's id, to look only in that tab.
promptNoWhat the section is about, in your own words. Omit to list the headings.
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

A4.2/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds 'Reads only; no run starts' plus a full account of the return contract: what `section` contains, what `confidence` and `considered` mean, and that `section` is null when nothing matches. With no output schema present, this return-value disclosure carries real weight.

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?

Purpose is front-loaded in the first clause, followed by mechanics, return shape, and the list-headings mode. The em-dash-heavy sentences are dense but each carries substantive information; only minor trimming is possible.

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, no enums, and five params, the description covers purpose, input semantics, the alternate no-prompt mode, and the full return shape including the null case. It is complete enough to call correctly; the only gap is explicit comparison against sibling section tools.

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 value beyond the schema by giving concrete `prompt` examples ('ATH434 in multiple system atrophy', 'the competitive landscape') that illustrate the intended natural-language phrasing. It does not clarify `sheet`, `context`, `llm_model`, but those are self-documented.

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?

States a specific verb and resource: it selects the prose section under a heading, matched from the caller's own description rather than a known heading name. The mechanism ('chosen from every heading in the workbook, as the document has them') is precise enough to distinguish it from tools that list or target sections, though it never names those siblings explicitly.

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?

It gives clear triggering context — use when you can describe what you want but do not know a heading ('you do not need to know a heading') — and states an alternative mode within the tool ('Omit `prompt` to list the headings instead'). It stops short of routing the agent against sibling tools like get_sections, list_sections or target_section.

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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