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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses valuable behavioral nuances: agent-labeled rows require include_unverified=true and remain labeled, pending event_links carry human_verified=false and review_status=pending_human_review, and interpreted fields like efficacy or PoS are never returned. This goes well beyond the structured annotation hints.

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 front-loaded with the core purpose and then elaborates on filtering and data provenance. It is appropriately sized for the tool's complexity, though the later sentences introduce specialized terms like 'AI-serviced event_links' that add density but still earn their place.

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 output schema exists and annotations declare the tool read-only and idempotent, the description fills the remaining gaps: source-verified defaults, agent-labeled handling, pending review statuses, and explicit exclusions of interpreted results. The description is fully adequate for correct invocation.

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 all three parameters are already documented. The description adds context beyond the schema by explaining the behavioral implications of include_unverified and confirming that rejected rows are never returned, which enriches parameter understanding without contradicting schema details.

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 opens with a specific verb and resource: 'Fetch the published Evidence Atlas program universe for a sub-indication.' This clearly distinguishes the tool from siblings like get_dossier or query_benchmarks by its focus on program universes keyed by sub-indication.

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 clear context on when the tool is appropriate: fetching published program universe data, with defaults to source-verified rows and explicit handling for agent-labeled rows. It does not name sibling alternatives or state when not to use it, but the usage context is sufficiently implied by the scope and filtering behavior.

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