optimize_fleet_assignment

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

With no annotations, the description carries the full burden. It discloses the optimization criteria (minimize spool swaps, prefer color matches) and that the tool returns assignments (not executing them). However, it does not clarify if the tool modifies any state, requires printer locks, or handles edge cases like insufficient materials.

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 extremely concise: a single-line summary followed by a bullet-point parameter explanation. Every sentence adds value with no repetition. It is front-loaded with the core purpose, making it easy for the agent to quickly understand the tool's role.

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 simple tool with one parameter and no output schema, the description covers the input format and optimization objective. However, it fails to describe the return structure explicitly (e.g., type of assignments, mapping of jobs to printers), which is needed for the agent to parse the output correctly.

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 coverage is 0% with a generic array of objects. The description adds critical meaning by specifying the expected job dict fields (file_name, material_type, required_grams, optional color) and their purpose. This compensates well for the schema's lack of detail, though it could further explain constraints on the job list (e.g., maximum size).

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: 'Assign print jobs to printers by material availability.' It specifies what the tool does (optimize assignments) and the resource (print jobs and printers), distinguishing it from siblings like 'route_print_job' which likely focuses on routing individual jobs.

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 implies usage when you have a list of print jobs to assign optimally based on material availability and color preferences. However, it does not provide explicit guidance on when not to use this tool or mention alternatives such as 'route_print_job' for single-job assignment, leaving the agent to infer context.

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