validate_cochrane_data

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

With no annotations provided, the description carries full burden. It discloses the checks performed, which is useful, but does not mention any side effects, output format, or whether data is modified. For a validation tool, read-only behavior is implied but not explicit.

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 two sentences, front-loaded with the primary action, and no wasted words. It is highly concise while conveying the core purpose.

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 absence of an output schema, the description should indicate what the tool returns (e.g., a validation report, pass/fail). It also doesn't explain differences between 'basic' and 'comprehensive' validation. However, it does mention the key checks, providing some completeness.

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 coverage is 100%, so baseline is 3. The description only mentions general validation areas, not the parameters themselves. It doesn't clarify what 'validation_level' does beyond the schema's brief 'Level of validation checks'.

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 ('Validate') and resource ('study data'), and lists concrete checks (PICO criteria, sample sizes, effect sizes, data quality), clearly distinguishing it from sibling tools like import_revman_data or generate_forest_plot.

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 implies a validation workflow but does not explicitly state when to use this tool vs alternatives, nor provide prerequisites or exclusions. It lacks clear when-to-use guidance.

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