parallel_map

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

With no annotations, description must carry full burden. It discloses parallel execution, fan-out network, return format (one result per item with Proof-of-Execution hash), and independence requirement. Lacks details on failure handling or limits, but adequate for high-level understanding.

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 with no fluff, front-loaded with key verbs 'Run the SAME instruction over MANY items in parallel'. Every phrase adds info.

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?

Given no output schema and no annotations, description covers purpose, usage, return structure, and independence. Missing details on max items, timeout, or error handling, but sufficient for initial selection.

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% (baseline 3). Description adds value by explaining model parameter as optional and linking to network_status, and emphasizes that items are independent and instruction is the same for all. This enriches the schema.

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?

Description clearly states the tool runs the same instruction over many items in parallel, distinguishing it from siblings like infer (presumably single-item) by emphasizing bulk processing. It also references network_status for model IDs, reinforcing distinction.

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?

Explicitly recommends use for bulk classify/extract/summarize/translate with independent items, advises against looping single calls, and directs to network_status for model info. Provides clear when-to-use and alternatives.

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