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Dataset totals and medians

hub_stats
Read-onlyIdempotent

Returns dataset totals: business, ranked and lesson counts, counts per category and tier, median annualized revenue, payment-verified share, latest change date. Example: {} -> {business_count, ranked_count, median_annualized_usd, ...}. Cost: free (100 calls/day without a key).

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely new behavior: it is free and capped at 100 calls/day without a key, which matters for call budgeting. It also preempts the empty-argument call pattern, though the field preview overlaps with the output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

Three short sentences, front-loaded with the returned aggregates, then a call-shape example, then the cost caveat. No filler and no restatement of the title. Each sentence carries distinct information.

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?

An output schema exists, so the description need not fully document return values, yet it names the key fields, gives the empty-argument call shape, and discloses the rate limit. For a zero-parameter read-only aggregate tool that is everything an agent needs before invoking.

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?

The tool takes zero parameters, so per the rubric the baseline is 4. The description correctly communicates that the call takes an empty object ('Example: {} -> ...'), which prevents an agent from hunting for filter arguments. Nothing further is needed.

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 verb and resource (dataset totals) and enumerates the aggregates returned — business/ranked/lesson counts, per-category and per-tier breakdowns, median revenue, verified share. An agent can tell this is the dataset-wide summary tool rather than a per-business or list tool from the sibling set. It stops short of explicitly naming the siblings it displaces (e.g. leaderboard, search_businesses) as alternatives.

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 only implied: the enumerated aggregates signal 'use this for an overview or baseline before drilling into individual businesses.' There is no explicit when-to-use, when-not-to-use, or named alternative. The cost note (free, 100 calls/day without a key) gives the agent a practical budget signal but does not route it between tools.

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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