get_research_method

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint). The description adds useful behavioral context beyond those annotations by explaining the return payload: protocols, guidance, evidence, and a checklist. It does not discuss edge cases like empty search results or content availability for obscure methods, but for a read-only lookup tool this is not a significant gap.

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 dense but well-organized: the first sentence establishes purpose and enumerates the full category map, the second defines the return structure, and the third gives the use case. No sentence is filler or redundant with the schema annotations.

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?

There is no output schema, so the description compensates by specifying exactly what the tool returns ('protocols, do/don't guidance, evidence... checklist'). It also covers parameter categories, use cases, and the safe read-only nature of the call. An agent has enough information to call this tool correctly in the intended contexts.

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 schema covers both parameters at 100% with meaningful descriptions, including the category enum and the default value. The description broadly repeats the category families ('qualitative', 'quantitative', 'usability') but adds no parameter-level depth beyond the schema. Baseline 3 is appropriate because the schema carries most of the parameter-semantic weight.

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 uses a specific verb with a specific resource: 'Get research method details — qualitative... quantitative... or usability...' It also enumerates what is returned ('protocols, do/don't guidance, evidence, and a checklist'), which clearly distinguishes it from sibling knowledge-retrieval tools like get_principles or get_checklist. The explicit category breakdown leaves no ambiguity about the tool's scope.

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 closes with a clear when-to-use instruction: 'Use when the user is designing a study or asking how to measure something.' It provides a solid usage context without explicitly naming alternative tools or exclusions, so it earns a 4 rather than a 5.

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