offload_work

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

No annotations exist, so the description carries the full burden. It fails to disclose important behaviors such as whether Ollama must be running, error handling if the model fails, permission requirements, or cost implications beyond token savings.

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 are concise and front-loaded with the primary purpose. Every word contributes meaning, leaving no redundancy.

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 6 parameters fully documented in schema and no output schema, the description covers core functionality well. It could mention what the output generally looks like (e.g., returned text) but is sufficient for basic understanding.

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 all parameters. The description adds value by listing example use cases (code generation, refactoring) but does not augment parameter meaning beyond 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?

The description explicitly states the tool offloads coding/text tasks to a local LLM to save API tokens. It distinguishes from sibling tools like get_metrics and list_loaded_models by focusing on task execution rather than system queries.

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?

It suggests using the tool for routine tasks like code generation and refactoring to save tokens, but does not specify when to avoid use (e.g., for critical tasks requiring Claude's reasoning) or mention alternatives like batch_offload for bulk operations.

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