get_research

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

With no annotations, the description carries the full burden. It discloses that the tool reads from a local directory, never throws errors, returns an empty store if files are missing, and describes the return structure with evidence links. It could add that it does not modify data, but 'loads' implies read-only.

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?

Concise and well-structured: opens with purpose, then prerequisites, error behavior, and use cases. Every sentence adds value with no redundancy, making it easy to parse 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?

Despite lacking an output schema, the description comprehensively details the return shape including fields like version, sources, observations, findings, themes, personas, quantitativeMetrics, and evidence links. It also explains error behavior and prerequisites, making the tool fully understandable.

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 tool has zero parameters, so baseline 4 applies. The description adds no parameter info because none exist, but it explains the input as the implicit state (the local directory), which is sufficient.

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 specifies a specific action ('Load and return') and resource ('project's user research V2 store'), detailing contents like observations, findings, personas, and quantitative metrics. It distinguishes from sibling tools by contextualizing its use with compose and other research tools.

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?

Explicitly states when to use: before compose with research-driven intent, to inspect research context, or to verify imports. Also notes prerequisites (none) and suggests combining with compose, providing clear context despite not listing exclusions.

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