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Search workbooks

search_workbooks
Read-onlyIdempotent

The workbooks whose title, latest prompt or current text contain every word of query (any order, not case-sensitive), each with a snippet around the first match. Title matches come first, then prompt matches, then the most recently changed. A phrase such as 'tea importers' or 'weekly ops' finds the workbook it names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesWords to find, e.g. 'tea importers' or 'weekly operations'.
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

A4.1/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive behavior, so safety needs no restating. The description adds genuine behavioral context beyond them: snippet generation around the first match and a deterministic ranking (title matches first, then prompt, then most recently changed). It omits any result-count cap or pagination behavior, keeping it just short of a 5.

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?

Two sentences, well front-loaded: matching semantics and ranking lead, with the example phrase last. Efficient overall, though the 'tea importers'/'weekly ops' example partially duplicates the example already in the query parameter's schema description, so it does not fully earn its place.

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, the description carries the return-shape burden and does so adequately: a matching set with snippets and a defined order. It does not address result limits, empty-result behavior, or whether all matches are returned, which is a minor gap for a search tool.

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, and the description earns above that by elaborating the `query` semantics the schema only gestures at: all words must appear, any order, case-insensitive, phrases resolved to named workbooks. The context/llm_model params are fully documented in-schema and need no further prose.

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 (search) and resource (workbooks), and precisely defines what is searched (title, latest prompt, current text) and how matching works (every word, any order, case-insensitive). This is enough to distinguish it from find_workbook and list_workbooks without opening a sibling schema.

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?

Usage is implied by the phrase-search semantics ('a phrase such as tea importers finds the workbook it names'), which suggests when it beats name lookup. However it never explicitly contrasts with find_workbook or list_workbooks, nor states when NOT to use it. Adequate but leaves the routing decision 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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