Account-level print statistics (success rate, filament used, print time, cost) with optional date range and user/printer filters.
| Name | Required | Description | Default |
|---|---|---|---|
| users | No | ||
| general | No | ||
| end_date | No | ||
| printers | No | ||
| fake_data | No | ||
| start_date | No | ||
| printer_models | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds useful context about the included metrics and optional filters, but does not disclose default behavior (e.g., whether unfiltered calls return all account data) or return format. This is adequate but not rich behavioral context beyond what annotations provide.
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?
The description is a single, compact sentence that front-loads the core purpose and then mentions optional filters. It is appropriately sized with no wasted words, making it easy to parse and process.
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?
With 7 parameters and no output schema, the description does a reasonable job by listing the returned metrics, but it omits details about the meaning of several parameters and any default behavior. For a statistics tool with moderate complexity, the description is adequate but leaves room for clarification around filter semantics and response structure.
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?
Schema description coverage is 0%, and the description only partially compensates by mentioning 'date range and user/printer filters', which vaguely maps to start_date, end_date, users, and printers. However, parameters like 'general', 'fake_data', and 'printer_models' are left completely unexplained, leaving significant gaps for agent understanding.
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 the tool's purpose as providing account-level print statistics, listing specific metrics (success rate, filament used, print time, cost) and optional filters. It effectively distinguishes this from sibling tools like get_farm_overview or get_referral_stats by specifying the account scope and metric types, though it lacks an explicit verb like 'retrieve' or 'get'.
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 indicates the optional date range and user/printer filters, implying it is used for filtering statistics, but provides no explicit guidance on when to choose this tool over alternatives. There is no mention of exclusions or when not to use it, and sibling tools like get_farm_overview are not referenced for comparison.
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.
Most tools are clearly separated by resource and action, but several overlapping queue-related tools exist—get_next_queue_item vs get_next_queue_items_for_printers, list_queue vs list_pending_queue_items, and inspect_printer_queue—which could cause misselection. list_custom_fields vs list_custom_fields_for and add_to_queue vs create_print_job add further ambiguity.
Names overwhelmingly follow a verb_noun snake_case pattern with consistent resource nouns like print_job, queue_item, folder, and filament. Minor inconsistencies exist, such as add_to_queue vs create_folder/create_print_job, save_queue_group vs update/create, and the slightly odd home_printer and send_back_for_revision.
With 74 tools, this is far beyond the recommended MCP server size and makes the toolset unwieldy to navigate, even for a broad domain. This clearly falls into the extreme 50+ tool category.
Core operational workflows—queue, print jobs, files, filament, and printer control—are well represented. However, printer lifecycle management is missing (no add/update/delete printer), and maintenance management is limited to a read-only dashboard with no corresponding actions.
