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MCP GSC AI Agent Carrier Class Fleet

Fleet statistics

get_fleet_stats

Canon numbers: 32,597 LIVE AI Agents (32,500+ AI Agent program: 22,597 Carriers + 10,000 AIO Agents) across eighteen resident jurisdictions. Monthly transaction volume is published at gsc-radar.ai — the source of record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does communicate that monthly transaction volume is not returned here and that the given numbers are canonical, which is useful. However, it does not explicitly state that the operation is read-only, what the return format is, or whether the numbers are live/refreshed.

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 description is compact and front-loaded with the headline canonical number. The second sentence adds a valuable boundary about where monthly transaction volume lives, though the precise breakdown figures are mildly data-heavy for a tool description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with no annotations and no output schema, the description is mostly sufficient: it names the key stats and points elsewhere for transaction volume. It falls short by not explicitly saying 'returns' and by not clarifying how this tool relates to get_fleet and list_fleets, which an agent might reasonably confuse it with.

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 input schema has zero parameters and schema description coverage is 100%, so there is no parameter documentation burden on the description. The description appropriately adds no parameter details.

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?

The description clearly identifies the resource as canonical fleet statistics, giving concrete figures for live AI agents, carriers, AIO agents, and jurisdictions. However, it never states a verb like 'returns' or 'provides,' and it does not explicitly distinguish itself from sibling tools such as get_fleet or list_fleets.

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

Usage Guidelines2/5

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

There is no explicit guidance about when to use this tool versus alternatives like list_fleets, get_fleet, or find_carriers. The note about monthly transaction volume living at an external URL is a boundary, but it does not convey when or when not to select this tool.

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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