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Get usage / quota

get_usage
Read-onlyIdempotent

Return the caller's current MCP quota: plan, tokens used, remaining and limit (free starter = 500 tokens total forever; paid plans = 40,000/month ≈ 2,000 default-page searches; Enterprise = personal limit via support). Token costs: 1 per delivered result (an ad/webmaster/advertiser row, a trends row, an entity card), ad media = 1, creative media = 10, TikTok row = 100. You pay only for results actually delivered (short pages auto-refund). This call is FREE — it consumes no quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
planYes
usedYes
limitYes
windowYes
reset_atNo
remainingYes
unlimitedNo
server_versionNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations, the description discloses the key behavioral fact that the call consumes no quota, and it explains the broader cost model including auto-refund behavior for short pages. It also clarifies plan-specific limits, adding meaningful context not present in 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.

Conciseness5/5

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

The description is information-dense but well organized, front-loading the core purpose before plan details and cost rules. Every sentence adds value: the plan breakdown, token costs, and the free-call note are all relevant to using the tool correctly.

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?

With no parameters and an output schema present, the description fully covers the situational and behavioral context an agent needs: what the quota is, how plans differ, how token costs are computed, refund behavior, and that the call itself is free.

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 has zero parameters and the schema is an empty object, so there is nothing for the description to document. The baseline of 4 applies because no parameter explanation is needed and the description doesn't mislead about parameters.

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?

The description opens with a specific verb and resource: "Return the caller's current MCP quota." It enumerates the included fields (plan, tokens used, remaining, limit), making the tool's function unambiguous and distinct from sibling data-fetching tools.

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?

The description provides clear context: it explains plan variants, token costs, and that "This call is FREE." It doesn't explicitly name alternatives or exclusion criteria, but the tool is unique among siblings and its purpose is so evident that no alternative-routing is needed.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool pairs a distinct action verb with a distinct entity type: search_* queries databases, get_* fetches single-entity cards, find_similar_* performs similarity lookups, and add_to_favorites/list_favorites handle saved items. The three find_similar tools explicitly cross-reference their differences (visual vs entity overlap vs URL-parameter), and get_adversus search_advertisers are clearly single-card vs typeahead.

Naming Consistency5/5

All 21 tools follow a consistent verb_noun snake_case pattern: six search_* tools, ten get_* tools, three find_similar_* tools, plus add_to_favorites and list_favorites. No camelCase, no mixed verb styles, and the similarity trio shares an identical prefix, making the set highly predictable.

Tool Count4/5

At 21 tools this is above the typical 3-15 sweet spot, but the server spans six entity domains (ads, creatives, advertisers, webmasters, hubs, shops) plus cross-cutting media delivery, favorites, trends, analytics, similarity and usage. Nearly every tool maps to a distinct workflow step, so the set feels dense rather than bloted.

Completeness4/5

The core lifecycle is fully covered: search every entity type, fetch detail cards, download media, save to favorites, list favorites back, plus trends, analytics, similarity and TikTok surfaces. The main gaps are a remove_from_favorites operation and a standalone hub detail card, neither of which blocks the primary ad-intelligence workflows.