analyze_test_coverage

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

With no annotations, the description carries the full burden. It communicates that the tool is a read-only analysis operation through verbs like 'parse', 'identify', and 'suggests', and it discloses the ranking behavior by naming complexity, export status, and parameter count.

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?

Two tightly written sentences deliver the tool's purpose, inputs, and ranking criteria with zero filler. The core behavior is front-loaded and every phrase adds information.

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?

The description adequately conveys what inputs are needed and what the output will contain at a high level. Since there is no output schema, a bit more detail about the returned suggestion format would help, but the core usage is clear enough for successful 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 description coverage is 100%, so the schema already documents all three parameters. The description adds no parameter-level detail beyond the schema, which keeps it at the baseline 3.

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 action ('Parse source code and test code') and a concrete result ('identify untested functions'), then adds ranking criteria. This clearly distinguishes it from siblings like generate_tests, which would produce tests rather than analyze gaps.

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 use case is inferable: use this when you need to know which functions lack tests. However, it never explicitly states when not to use it or mentions alternatives, so the agent is left to infer the boundary with generate_tests and the other generation tools.

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