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Glama

get_available_governments

Read-only

Lists unlocked governments with policy slot counts and bonuses, so you can compare alternatives before changing government.

Instructions

Governments we have unlocked, with policy slot counts and their bonus.

get_policies reports only the CURRENT government, so nothing listed the
alternatives or what they do — which made change_government unusable in
practice. Slot counts matter because they determine how many policy cards of
each type can be slotted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.11

TDQS

A3.8/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds context about what the tool returns (unlocked governments, slot counts, bonuses) and why it matters (slot counts determine policy card capacity). It doesn't describe return format or pagination, but with zero parameters and a read-only annotation, the description adds adequate value.

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 three sentences and front-loads the core purpose. The second and third sentences provide useful context about why this tool exists and why slot counts matter. It's slightly longer than strictly necessary, but every sentence earns its place by explaining the tool's value relative to get_policies and change_government.

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?

For a zero-parameter read-only tool with an output schema, the description is complete enough. It explains what the tool returns, why it's needed, and how it relates to the government-change workflow. The only minor gap is not explicitly naming get_policies as the sibling alternative, but the reference is clear enough.

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, so the schema provides no parameter documentation. The description compensates by explaining what the output contains (governments, policy slot counts, bonuses) and why slot counts matter. With 0 params, baseline is 4, and the description meets that baseline by giving meaningful context about the returned data.

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 states the tool lists unlocked governments with policy slot counts and their bonus, which is a specific verb+resource. It also distinguishes itself from get_policies by noting that get_policies only reports the current government. However, it doesn't explicitly name the sibling get_policies as the alternative, though it is clearly implied.

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 explains why this tool exists: get_policies only reports the current government, so this tool is needed to see alternatives before using change_government. This gives clear context for when to use it. It doesn't explicitly state when not to use it or name alternatives, but the context is strong enough to guide an agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.