run_health_check

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds real complementary context: the composite score's weighting ('blending automation, coverage, and manual execution'), and the subtle rule that the score only considers sections that 'are present and have data.' No contradiction with annotations.

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?

Front-loaded with the purpose, then a dense but purposeful enumeration of the output sections and the include parameter. A single compact paragraph is slightly heavy, but every sentence carries meaning; nothing redundant or off-topic.

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?

With no output schema, the description shoulders the burden of character return shape, and it does so thoroughly: composite score, sub-scores, sections, and weighting behavior. Minor gaps like undefined behavior when a project has no data are acceptable for a read-only analysis tool. The three params are fully covered between schema and description.

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 the baseline is 3; the description earns a 4 by adding meaning beyond the include parameter's enum declarations — detailing what each section computes (pass rate, flakes, status/priority breakdown, pass-rate trend) and how section selection affects the composite score.

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?

States a specific verb and resource ('Get a holistic health assessment for a project') and defines the output precisely (composite score 0-100 plus per-dimension sections). It is clear and distinctive against the CRUD-heavy siblings, though it does not explicitly contrast itself with the analytics siblings (get_coverage_gaps, get_launches_statistics, get_launches_trends).

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 the use case from 'holistic health assessment' and gives operational guidance ('Use include to limit which sections are computed'), but it never states when to prefer this tool over more targeted alternatives or when not to use it. The context is clear but the routing to/from siblings is left to inference.

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