get_population_snapshotLatest GDC population snapshot (the weekly 'Friday report'):
total_in_gdc_custody (every GDC bed type, including probation detention
centers and RSAT), the jail backlog, and the facility-type components.
The total is the ONLY population figure; the components are parts of it,
not alternative totals.| 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 read-only and non-destructive behavior. The description adds the weekly cadence, the scope of included bed types, and a critical semantic caution that components are parts of the total, not alternative totals. This adds value beyond 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short, focused sentences with no filler. The key resource is front-loaded and the semantic caveat is placed immediately after the component list, earning its place.
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 read-only tool with rich annotations, the description sufficiently explains what data is returned and how to interpret the total. A slight gap is the lack of an explicit return shape for the jail backlog and facility-type components, but the description is still complete enough for correct invocation.
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 100% schema coverage, so there is nothing for the description to explain. The baseline for zero-parameter tools is 4, and no parameter ambiguity exists.
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 uses a specific verb and resource ('Latest GDC population snapshot') and names the exact contents: total_in_gdc_custody, jail backlog, and facility-type components. It is clearly distinguishable from sibling tools focused on contraband, facilities, mortality, and other data.
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?
It establishes clear context by identifying the weekly 'Friday report' and latest snapshot, making it obvious when an agent needs current population custody data. It does not explicitly name alternatives or exclusions, but the semantic warning about the total being the only population figure provides useful guidance on interpretation.
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.