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hedwigai

by com.hedwigai
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Put a workbook in your shell

checkout_workbook
Read-onlyIdempotent

Writes the workbook into your shell session's /tmp so you can work on it with bash. A workbook's tabs, and the spreadsheet they were derived from, live in this account's object store — the shell mounts providers, not that store, so this is the only way to open them from bash.

It goes into THIS connection's shell — the one the bash tool runs in — unless session names another: a shell's id is what session id prints inside it. /tmp belongs to one session, so a checkout made in one shell is not visible in another.

Answers with the directory, the file to EDIT, and a file per tab. For a workbook made from a spreadsheet the file to edit is current.xlsx and the tab markdown beside it is derived — editing that changes nothing. Read README.txt in the directory: it says which, with the xlsx commands for it — xlsx FILE get, set, insert-rows and the rest, every formula recalculated on save. Then commit_workbook with the same session. A workbook linked to a Drive or Dropbox file is checked out as the file stands now, and committing writes it back.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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."
sessionNoOptional: another shell's id, what `session id` prints inside it. Omit for this connection's shell.
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

A4.6/5.0
Behavior5/5

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

Despite readOnly/idempotent annotations, the description adds substantial behavioral context: session scoping (a checkout in one shell is invisible to another), the exact return contents, that derived tab markdown is non-authoritative (editing changes nothing), that current.xlsx is the file to EDIT, and that README.txt documents the xlsx commands. None of this is captured by 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.

Conciseness4/5

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

Purpose is front-loaded in the first sentence, and the remaining sentences carry real information (session scoping, return shape, commit follow-up, README pointer). It is dense and slightly long across three paragraphs, but few sentences are wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by describing exactly what is returned (the directory, the file to edit, per-tab files) and the read-README workflow. Combined with the session scoping and commit follow-up, an agent has everything needed to call and use the tool correctly.

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. The description nonetheless adds meaning: it clarifies that `session` names another shell whose id is what `session id` prints, and that omitting it targets THIS connection's shell. It does not expand on the workbook id or analytics-only params.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource (check out a workbook into the shell's /tmp) and explains the mechanism that makes it necessary (tabs live in the object store, which the shell does not mount). It is clearly distinguishable from siblings like read_workbook, pull_workbook, and commit_workbook.

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 context: use this to open a workbook for bash work, then call commit_workbook with the same session. It also states the linked Drive/Dropbox checkout semantics. It stops short of explicitly contrasting with sibling alternatives such as read_workbook or pull_workbook, which would be needed for a 5.

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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