MidOS Research Protocol

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool stores data for future learning, implying a write operation, but doesn't cover critical aspects like permissions needed, whether storage is permanent or reversible, rate limits, or how the data is structured. This leaves significant gaps for a mutation tool with zero annotation coverage.

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 well-structured and concise, with a clear purpose statement followed by a bullet-point-like 'Args' section. Every sentence earns its place by defining the tool and its parameters without redundancy, making it easy to scan and understand quickly.

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 the tool has an output schema (which handles return values), the description focuses on input semantics adequately. However, as a mutation tool with no annotations, it lacks details on behavioral traits like side effects or error handling. The parameter explanations help, but overall completeness is moderate due to missing usage and transparency elements.

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?

The description includes an 'Args' section that explains each parameter's meaning: 'task_type' as the type of task (with examples), 'input_preview' as a brief description, and 'success' as a boolean indicating task outcome. With schema description coverage at 0%, this adds substantial value beyond the bare schema, clarifying semantics for all three parameters effectively.

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's purpose: 'Store a new episodic memory/reflection for future learning.' It specifies the verb ('Store') and resource ('episodic memory/reflection'), making the action explicit. However, it doesn't distinguish this tool from its sibling 'episodic_search', which likely retrieves rather than stores memories, so it misses full differentiation.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as when to store memories (e.g., after task completion), or compare it to siblings like 'episodic_search' for retrieval. Usage is implied by the purpose but lacks explicit context or exclusions.

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