kratos_list_solvers

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It explains that the tool lists known solver_type values per analysis type and also scans for *_solver.py modules, which is informative. However, it does not disclose the effect of the optional analysis_type parameter (whether it filters results) or what happens when it is null, leaving a 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?

The description is a single sentence that packs a lot of information: it lists the analysis types, the pairing with Python modules, and the additional scan for all *_solver.py files. It is front-loaded with the core action and avoids unnecessary words, but the sentence is somewhat long and could be split for readability.

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 tool is relatively simple, and the description conveys the main output: solver_type per analysis type and modules found. However, it fails to explain the role of the analysis_type parameter, which is a critical part of using the tool correctly. Given the output schema exists, return values are covered, but the missing parameter behavior leaves the description incomplete for a robust understanding.

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?

The input schema has one optional parameter (analysis_type) with 0% description coverage. The tool description does not mention this parameter at all, nor does it clarify whether providing an analysis_type filters the listing or how the default null behaves. This leaves the parameter's semantics unexplained, requiring the agent to infer from the tool name and description.

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 uses the specific verb 'List' and clearly identifies the resource: solver_type values per analysis type (structural, thermal, fluid, potential_flow), plus Python modules. This distinguishes it from sibling list tools like kratos_list_elements or kratos_list_variables, which target different entities.

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 makes it clear that this tool is for enumerating known solver types and their implementing modules, giving the agent enough context to select it when needing solver information. However, it does not explicitly mention when not to use it or point to alternatives, so a small deduction.

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