Skip to main content
Glama

hedwigai

by com.hedwigai
OverviewDiscussionsAdmin

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}) or null.

Input Schema

TableJSON 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

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description still adds real behavioral value: 'nothing is made up', the always-present 'none of these' fallback, and the exact return shape `found` ({id, name, folder}) or null. That 'no fabrication / may return nothing' trait is not derivable from 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?

Front-loaded with the core behavior (one-or-none, fuzzy match) before the folder rules and return shape. The quoted example prompts cost some length but earn their place by demonstrating the expected input style; no sentence is pure filler.

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?

No output schema exists, and the description compensates by describing the return value (`found` with id/name/folder, or null) and the fuzzy-match contract. Combined with annotations covering safety, an agent has what it needs to call this correctly, though the relationship to sibling lookup tools remains unstated.

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 description coverage is 100%, so the baseline is 3. The description goes further on `prompt`, explaining the fuzzy-match/no-spelling-required behavior ('no spelling has to match', jev chooses), which the schema's terse 'in your own words' does not, and restates the folder scoping in matched terms.

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 action clearly: resolve a freeform word-prompt to exactly one workbook by name, returning `found` or null. That verb+resource+cardinality (one-or-none) is unambiguous. It stops short of naming the sibling it differs from — find_folder for folders, search_workbooks/list_workbooks for broader listing — so differentiation is left to inference.

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 conveys usage indirectly through the phrasing advice ('Say it the way you would ask') and the folder scoping rules, which tells the agent this is a natural-language single-match lookup. But it never states when to prefer this over search_workbooks, list_workbooks, or find_folder, and names no exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All hedwigai toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources