get_parole_statisticsGeorgia parole decisions by year — the series GPS assembled from the State Board of Pardons and Paroles' own annual reports, with the metric definitions and the instruments note that says how they were built.
| Name | Required | Description | Default |
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnly, idempotent, and non-destructive behavior. The description adds useful context about how the series was assembled and that metric definitions and an instruments note are included, but it does not describe the return format or any limitations. This is reasonable but not rich behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence front-loads the core subject ('Georgia parole decisions by year') and then adds source and documentation context. No wasted words, and the structure is immediately understandable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter, read-only tool, the description provides enough context to understand the data source and scope. It does not spell out the return shape, but the absence of an output schema and the simple nature of the tool make the description sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the description has no parameter semantics to explain. The schema is empty and there is nothing further needed to invoke the tool correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific resource — Georgia parole decisions by year — and identifies the source series with provenance. This clearly distinguishes it from sibling tools like get_population_snapshot or get_system_statistics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates this is the tool for the GPS-assembled parole statistics series, providing enough context to know when to select it. It does not explicitly exclude alternatives, but none of the siblings target the same data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool names a distinct resource or action: contraband incidents vs summaries, a single facility vs facility list vs staff roster, and site-wide search vs quote search. The few close pairs are complementary rather than duplicative, and descriptions explicitly indicate when to use each.
All tool names follow a consistent snake_case verb_noun pattern (get_*, list_*, search_*). The one outlier, get_gps_guide, still fits the get_<resource> convention, so there is no real style clash.
At 18 tools the server is heavier than a minimal CRUD surface, but it covers a broad public-data domain with a distinct endpoint per dataset or site function. The count feels slightly over the typical sweet spot rather than bloated or redundant.
The major GPS data domains are represented: facilities, population, mortality, contraband, parole, length of stay, settlements, and legal text. A few potentially relevant datasets (e.g., a structured budget tool) are not exposed directly, but search_site plus get_page cover most of those gaps.