get_optimization_plan

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

With no annotations provided, the description fully carries the burden. It details the analysis logic (flake scoring, broken test detection, pattern mining) and discloses that results are written to a file (optimization-plan.md), which is a write operation with potential persistence effects.

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 a single dense paragraph covering complex logic. While each sentence is necessary, it could be better structured (e.g., bullet points) to improve readability for an AI agent.

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 thoroughly explains the three analysis layers and their logic. However, it only states that the tool returns 'structured JSON' without detailing the return format, and it lacks information about error handling or prerequisites, leaving minor gaps.

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% with parameter descriptions already explaining history_limit and telemetry_limit. The description does not add extra meaning beyond what the schema provides, so the baseline score of 3 applies.

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 produces a three-layer analysis (test suite quality, MCP usage patterns, AI test generation effectiveness) and writes results to a file. This distinguishes it from sibling tools like get_test_history or analyze_url, which have narrower scopes.

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 explains that the tool is automatically triggered after run_tests, so it is used to 'read results in real-time.' This provides clear context, though it does not explicitly list when to avoid using it or name alternatives.

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