cmmn-execute_task

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

No annotations are provided, so the description must disclose all behavioral traits. It explains that process/decision/case tasks execute automatically and human tasks return an error. It also reveals the important behavior that the LLM may need to execute prompts inline and call cmmn-complete_task. However, it lacks details about synchronous/asynchronous behavior, error handling, or side effects, which would be valuable for a mutation tool.

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?

The description is two sentences plus a bold IMPORTANT note. It is front-loaded with the core purpose, followed by an exception and critical usage guidance. Every sentence adds value with 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?

For a complex tool that executes tasks and may require LLM action, the description is incomplete. It does not describe the return value structure beyond mentioning action_required and prompt fields. There is no output schema, so the description should explain what the result looks like, error cases, or status information. This gap could lead to incorrect usage.

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 baseline is 3. The description does not add extra meaning beyond what is already in the input schema. The parameter descriptions in the schema already explain confirm_human, force, inline, etc., so the tool description offers no additional semantic value.

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 clearly states the tool executes a single task directly, distinguishes between automatic and human tasks, and explicitly names the alternative tool (update_task_status) for human tasks. It provides specific guidance on handling results with action_required or prompt fields, which differentiates it from siblings like cmmn-complete_task.

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?

The description explicitly tells when to use this tool (for executing tasks) and when not to (human tasks should use update_task_status). It also provides detailed instructions on what to do after execution if the result contains action_required='llm_execute' or a prompt field, giving clear context for the LLM's subsequent actions.

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