get_health_trends

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context about the data source (predict_bugs) and the exact return format (JSON with dataPoints), which goes beyond annotations. No contradictions; description aligns 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?

The description is concise (three sentences) and front-loaded with the main purpose and return structure. Every sentence adds value: defining metrics, data source, usage, and output format. No superfluous text.

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 3 parameters, no output schema, and good annotations, the description is complete. It explains the data origin (predict_bugs), the return structure, and the intended use case. An agent can invoke the tool correctly without additional information.

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 description coverage is 100% (all 3 parameters have descriptions). The description itself does not add additional parameter-level details beyond what the schema provides. Baseline 3 is appropriate as the schema already handles parameter documentation.

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 states the tool provides time-series health metrics (bug score, complexity, coupling, churn) for a file or module. It distinguishes from sibling tools like get_complexity_trend and get_coupling_trend by aggregating multiple metrics, making its purpose specific and unique.

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 specifies using the tool 'to track if a module is improving or degrading' and notes it is populated by predict_bugs runs, implying appropriate context. However, it lacks explicit when-not-to-use guidance or alternative tools for individual metrics, which would enhance clarity.

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