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List Tabzoola's named tab sets

list_named_sets
Read-onlyIdempotent

Use this when the user names a kind of binder rather than listing titles (estate planning, trust, exhibit 1-25, medical chart, HOA, IEP, audit workpapers, real estate closing, and many more): returns Tabzoola's ready-made tab sets matching the query, each with its section titles in order, tabs per set, the page that describes it, the exact price for 25 sets (paper, collated, 3-hole punched, setup included) and an orderUrl that opens the designer pre-filled. Read-only; purchases nothing. Pass the titles to draft_order or quote_price for other quantities or options.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum sets to return (default 25).
queryNoWords to match against set names, uses and titles, e.g. "estate", "exhibit", "hoa", "chart". Omit to list sets.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the 'Read-only; purchases nothing' line is largely reinforcement. However, the description goes well beyond annotations by disclosing return contents — section titles in order, tabs per set, the describing page, the 25-set price basis (paper, collated, 3-hole punched, setup included), and an orderUrl that opens a pre-filled designer. That is genuinely useful behavioral context for deciding whether a follow-up call is needed.

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?

The usage trigger is front-loaded and the closing routing sentence is tight. The opening sentence is long and parenthetical-heavy, but each clause (examples, return fields, price basis) carries information an agent needs.

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?

There is no output schema, yet the description enumerates the return fields well enough that an agent knows what it will get and when to escalate to draft_order or quote_price. Nothing material is missing for a two-parameter read tool.

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 description coverage is 100%, so both parameters (query, limit) are already documented in the schema, including the 'omit to list sets' behavior. The description adds no syntax or matching semantics beyond what the schema states, so the baseline 3 applies.

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 (list/return) and resource (Tabzoola's ready-made named tab sets) and pins the trigger condition precisely: 'when the user names a kind of binder rather than listing titles.' The concrete examples (estate planning, exhibit 1-25, HOA, IEP) make it unmistakable versus sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit when-to-use condition and names the alternative path: 'Pass the titles to draft_order or quote_price for other quantities or options.' That directly routes the agent between this tool and its two siblings with no inference required.

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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