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 mentions the search method ('vector similarity') but lacks details on permissions, rate limits, response format, or error handling. For a search tool with no annotation coverage, this is a significant gap in transparency about how the tool behaves in practice.

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 front-loaded with the core purpose in the first sentence, followed by a clear 'Args' section listing parameters. It's efficient with minimal waste, though the structure could be slightly improved by integrating parameter details more seamlessly rather than as a separate block.

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's moderate complexity (2 parameters, no annotations, but with an output schema), the description is minimally adequate. It covers the purpose and parameters but lacks behavioral context and usage guidelines. The presence of an output schema means return values don't need explanation, but overall completeness is limited by missing operational details.

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 0%, but the description adds basic semantics for both parameters: 'query' as a 'Search query describing the experience/task' and 'limit' with a default value. This compensates somewhat for the lack of schema descriptions, but it doesn't provide deep insights like query formatting or limit constraints, keeping it at a baseline level.

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 as 'Search episodic memory for similar past experiences using vector similarity.' This specifies the verb ('Search'), resource ('episodic memory'), and method ('vector similarity'), making it distinct from generic search tools. However, it doesn't explicitly differentiate from sibling tools like 'search_knowledge' or 'semantic_search', which prevents a perfect score.

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. With sibling tools like 'search_knowledge' and 'semantic_search' available, there's no indication of what makes 'episodic_search' unique or appropriate for specific contexts, such as searching memory versus general knowledge. This lack of comparative context limits its utility for an AI agent.

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