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

Beyond the annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=false), the description discloses non-obvious behavior: optimality is guaranteed for up to 60 jobs, and small instances (<=16 qubits) are additionally executed on a quantum simulator with a signed receipt recording both runs. That is real context an agent could not derive from structured fields. It stops short of 5 because it never says what persistent artifact the non-read-only call actually creates.

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 sentences, front-loaded with the outcome and then the algorithmic/scale caveats; nothing is filler. The second sentence is dense (algorithm name, job limit, qubit threshold, receipt) but each clause carries information.

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 4-parameter optimizer with no output schema, the description should describe what comes back and what is persisted. It only gestures at a 'signed receipt' showing both solutions, without shape or contents, and gives no guidance on how the call differs from neighboring car_* tools. Adequate but notably incomplete on the return side.

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 100%, so the schema already documents jobs, vehicles, cost, and max_jobs_per_vehicle including defaults. The description adds only the idea of a per-vehicle limit and the job types, which is marginal over the schema wording. Baseline 3 applies when the schema carries the parameter burden.

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 names a specific verb+resource with an optimization objective: assign every job (delivery, pickup, service call) to a vehicle at lowest total cost under a per-vehicle cap. That is far more than a restatement of the name. It does not explicitly contrast itself with routing-family siblings such as car_route, so it falls short of a 5.

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?

Usage is implied rather than stated: the agent can infer 'use this when you have a set of jobs and vehicles and want an optimal assignment'. There is no explicit when-not, and no named alternative for the routing/cost-matrix case, so the agent must infer boundaries from siblings on its own.

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