run_tests

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'real LLM evaluation' implying cost/latency, but does not disclose side effects (e.g., whether results are saved, if a run record is created), rate limits, or the response/output format. For a tool that executes tests, this is a significant transparency gap.

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 concise sentences. The first sentence front-loads the core purpose ('Run regression tests against a system prompt with real LLM evaluation'), and the second covers the crucial parameter decision. No filler or redundancy.

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 is adequate for a 6-parameter tool with full schema descriptions, but lacks information about return values or output format (no output schema), and does not explain the role of other parameters like tools, test_model, or test_case_ids beyond the schema. Given the tool's potential complexity (running LLM evaluations), the description should mention what the caller receives or what side effects occur.

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 adds meaningful semantic guidance by explaining the relationship between prompt_id and system_prompt, noting that prompt_id is 'from save_draft_prompt' and preferred for 'faster performance'. This goes beyond the schema's simple 'use one or the other' instruction.

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 immediately states the specific action 'Run regression tests' against a 'system prompt' with 'real LLM evaluation' for a specific company. This clearly distinguishes it from sibling tools like create_test_case or list_test_cases, which create or list rather than execute tests.

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 gives explicit guidance on parameter usage: 'Use prompt_id (from save_draft_prompt) for faster performance, or system_prompt for direct text.' This is practical guidance for when to use each option. However, it does not explicitly exclude alternatives or contrast with other tools, though no sibling tool performs the same test-running function.

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