suggest_printer_for_job

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the tool is advisory, does not override safety limits, and cross-references current printer availability. It also notes that rankings are based on success rates from historical outcomes. However, it does not detail how suggestions are computed when no historical data exists or how conflicts are resolved, which prevents a higher score.

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 well-structured with a concise main line, followed by a note, then a parameters block. It front-loads the primary purpose. The Args block is somewhat redundant with the schema but provides extra commentary. Overall, it is focused and not verbose, earning a score of 4.

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?

The tool lacks an output schema, so the description should clarify the return value. It mentions 'suggest the best printer' and 'rankings', which is ambiguous (single vs. multiple results). Given the complexity and the many sibling printer tools, more detail on the output format (e.g., list of printers with scores) would improve completeness. Nonetheless, it covers inputs and core behavior adequately.

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 schema has 0% description coverage, so the description must compensate. It does so effectively by explaining each parameter: file_hash is for matching previous prints, material_type filters by material (with example 'PLA'), and file_name is informational only. This adds significant meaning beyond the schema's bare titles.

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 states the tool's purpose: 'Suggest the best printer for a job based on historical outcomes.' It specifies a distinct verb ('suggest') and resource ('printer for a job'), differentiating it from siblings like 'discover_printers' (which lists printers) and 'find_printers_with_material' (which filters by material). The mention of ranking based on historical outcomes sets it apart.

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 explains when to use the tool: to get an advisory suggestion based on historical outcomes, optionally filtered by file hash or material type. It explicitly warns that suggestions are advisory and do not override safety limits, advising to run preflight validation. While it doesn't explicitly state when not to use it, the advisory note and context relative to siblings (e.g., 'discover_printers' for listing all printers) provide sufficient guidance.

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