clinical_trials_size_stats

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

Annotations already declare readOnlyHint=true, and the description complements this by outlining the specific read-only outputs. It adds value by listing what statistics are returned, though it does not mention whether there is any size limit on results or if the 'largest studies' list has a cap.

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 concise sentences that front-load the main purpose and then add a clarifying note about 'quick overview'. Every phrase earns its place and there is no redundancy or filler.

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 enumerates exactly what kinds of statistics will be returned, covering scope (total count), volume (average size, size distribution), and outliers (largest studies). For a zero-parameter, read-only stats endpoint, this is sufficient context for an agent to invoke it 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?

The tool has zero parameters and the schema reflects that with no properties. Description compensation is unnecessary, so the baseline of 4 applies. The description explains what the tool computes without any parameter details, which is appropriate for a no-input tool.

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 identifies the tool as returning database statistics with specific output types (total study count, average JSON size, size distribution, largest studies). This distinguishes it from sibling tools like clinical_trials_search or clinical_trials_detail, and even from clinical_trials_stats by focusing on size-related metrics.

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?

Provides clear context for use: 'Quick overview of the ClinicalTrials.gov database scope and data volume' tells the agent this is for high-level summaries rather than detailed record lookup. However, it does not explicitly name alternative tools for when the user needs individual trial data, so it stops short of providing exclusions.

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