get_drug_admissionsAdmissions to Georgia prisons on drug offenses for the latest published report year, broken down by sex, age band and related measures.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 declare the tool read-only, idempotent, and non-destructive, so the description does not need to repeat that. It adds useful context by noting the data covers the latest published report year and specific demographic breakdowns, but it does not disclose details like data source, update cadence, or exact return structure.
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?
A single, tightly written sentence front-loads the core subject ('Admissions to Georgia prisons on drug offenses') and efficiently adds temporal scope and breakdown dimensions. There is no filler or repetition of the tool name or title.
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 zero-parameter report-retrieval tool with no output schema, the description supplies sufficient context: geographic scope, topic, time period, and breakdowns. It could be slightly more precise about what 'related measures' includes or how the latest year is identified, but the tool is simple enough that this is a minor gap.
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, and the schema covers all of them by default, so the baseline is 4. The description adds value by indicating that the returned data is organized by sex, age band, and related measures, giving the agent a clearer idea of the fixed output dimensions.
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 clearly identifies a specific resource: admissions to Georgia prisons on drug offenses. It adds temporal scoping ('latest published report year') and output breakdowns ('sex, age band and related measures'), making the tool's purpose unambiguous and distinct from siblings like get_length_of_stay or get_population_snapshot.
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 usage context is implied: an agent can infer this tool is appropriate when drug-admission statistics for Georgia prisons are needed. However, it does not explicitly state when not to use it or mention any alternative tools, leaving the routing decision to inference.
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.