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hedwigai

by com.hedwigai
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Find a workbook from your own words

find_workbook
Read-onlyIdempotent

The one workbook your words mean, or none. Say it the way you would ask — 'Alterity's diligence memo', 'the weekly ops report' — and jev chooses among the account's workbooks by name, with 'none of these' always an option, so nothing is made up and no spelling has to match. folder limits where it looks: omit it for anywhere, / for workbooks at the top level only, or a folder path for that folder and everything in it. Answers found ({id, name, folder, and its title where it has one}) or null.

Input Schema

NameRequiredDescriptionDefault
folderNoWhere to look: omit for anywhere, `/` for the top level, or a folder path.
promptYesWhich workbook, in your own words.
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

A4.2/5.0
Behavior4/5

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

Annotations already cover readOnly/idempotent/non-destructive, and the description adds beyond them: it guarantees no fabrication, spelling need not match, 'none of these' is always available, and it documents the return shape (fields or null). It doesn't state match quality or ambiguity behavior when several candidates are close, a minor gap.

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?

Front-loaded with the tool's essence in the first clause, then the usage detail, then the folder semantics and return value. Mostly efficient, though the folder sentence duplicates the schema and could be tightened.

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?

No output schema exists, and the description ably supplies the return contract ('found' with id/name/folder/title, or null). Combined with read-only annotations and full parameter coverage, nothing needed to call it correctly is missing.

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 coverage is 100%, so the schema already documents all four parameters, including the folder modes and the prompt phrasing. The description's folder explanation merely restates the schema, and context/llm_model are untouched in prose. Baseline 3 is appropriate.

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?

States a specific verb and resource — it resolves natural-language words to a single workbook by name among the account's workbooks — and the 'one ... or none' framing distinguishes it from list/search siblings that would return many. An agent can tell what it gets back without opening the schema.

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?

Explains clearly when this applies (you have an approximate name in your own words) and what phrasing to use ('say it the way you would ask'). It also spells out the three folder modes with exclusions (omit/`/`/path, recursive). It does not name the alternative siblings like search_workbooks or list_workbooks, leaving that routing 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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