ai_task

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

With no annotations, the description carries the full behavioral burden, and it does add real context by disclosing the backing model (Claude) and per-call pricing ($0.01/$0.02), which affects invocation decisions. It omits auth requirements, rate limits, latency, and failure behavior, so the disclosure is useful but incomplete for a six-parameter tool with no annotations.

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 dense sentences: the first front-loads the core operation and maps each enum value to its behavior, the second carries cost information. No filler, no repetition, and the highest-value information (what it does, what it costs) comes first.

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 four-mode tool with no output schema, the description sketches returns for each mode (N-sentence summary, translation, label plus confidence score, structured JSON), which is enough to call it correctly. It stops short of fully specifying output shape (e.g., what summarize/translate return) and error behavior, which matters given no output schema exists.

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 100%, so the schema already documents every parameter, including the 'classify only'/'extract only' conditionals. The description reinforces the task-to-parameter mapping (N sentences, target language, your labels, JSON Schema) but adds little syntax or constraint detail beyond what the schema provides, matching the baseline 3.

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 and resource ('Run a Claude-powered text task') and enumerates the four distinct modes with their concrete outputs, so an agent knows exactly what the tool produces. It does not, however, distinguish itself from siblings like verify_fact or search_web, leaving the agent to infer when this generic task runner is the right choice.

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 through the per-mode enumeration (use `summarize` for N sentences, `classify` with your labels, etc.), which helps pick a mode but not a tool. There is no guidance on when to prefer this over verify_fact/check_market or on prerequisites such as required inputs per mode beyond what the schema already states.

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