atlas_case_study_lookup

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

The description adds behavioral details beyond annotations: default response is SLIM (truncated to 240 chars), include='full' for verbose narrative, and returns 404 when id is not found. Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the bar is lower, but the description still provides useful extra context like truncation and error behavior.

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 concise and well-structured: it opens with the core purpose, then key details (default slim, parameter guidance, usage flow, error behavior, rate limits). Every sentence adds value, and there is 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?

Given the tool's moderate complexity and the presence of annotations and an output schema, the description covers all essential aspects: purpose, input parameters (with usage hints), response format (fields listed), error condition (404), rate limits, and even hints at next steps (next_calls). It is complete enough for an agent to use correctly.

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 baseline is 3. However, the description adds meaning beyond the schema by explaining the default behavior for include and the format of case_study_id. It adds value by clarifying the impact of the include parameter and the expected ID pattern.

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 it looks up a MITRE ATLAS case study (a documented real-world AI/ML attack incident) and specifies that it links techniques to incidents. It also distinguishes from sibling tools like atlas_technique_search and atlas_case_study_search by advising to use it after atlas_technique_search, thus clarifying its purpose.

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 explicit usage guidance: 'Use this after atlas_technique_search to find which incidents have exercised a given technique' and hints at the next logical step via next_calls. It also mentions rate limits (Free: 30/hr, Pro: 500/hr). While it doesn't exhaustively list when not to use, the context is clear enough for an agent.

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