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

Adds substantial behavior beyond the annotations: returns a ticket that does not mean completion, requires polling via get_network_job, every accepted shard is recomputed on coordinator CPU with no net acceleration claim, and workers may be unavailable or jobs may expire. This is unusually rich disclosure for an async distributed job.

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?

Six dense sentences, all earning their place, with the core action and the simulate_pi distinction front-loaded and operational caveats trailing. No filler.

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 no output schema, the description correctly explains the return value (a ticket, queued != completed) and how to retrieve results. Combined with the constraint and caveats, an agent has enough to call and follow through 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 description coverage is 0%, so the description must compensate for the three parameters, yet it says nothing about seed, backend, or samples. The names are self-explanatory and the schema carries min/max bounds, defaults, and an enum, but the description adds no semantic value on any parameter.

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?

States a specific verb (Queue) and resource (public synthetic Monte Carlo pi_chunk_v1 job on contributor clients). It explicitly distinguishes itself from the sibling simulate_pi and notes the fixed 31-bit integer model, so an agent can tell the two apart without opening schemas.

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?

Names the alternative (simulate_pi) and the follow-up tool (get_network_job) for polling, and states an exclusion ('No files or private datasets are accepted'). It gives clear context but never states the decision condition for choosing this over simulate_pi.

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