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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the algorithmic approach, 'topological constraint propagation,' which gives some insight into how the tool operates. However, it does not mention side effects, output behavior, failure modes, or how the four parameters interact.

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, focused sentence with no filler or redundant phrasing. It front-loads the core purpose and adds benchmark scope and method, though the method phrase is somewhat jargon-heavy.

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?

With four undocumented parameters, no usage exclusions, and no annotation support, the description is not sufficient for an agent to reliably invoke this tool. The output schema exists, so return-value documentation is not the main gap, but the input contract and selection logic remain underspecified.

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 0%, and the description itself provides no parameter-level details. The parameters 'question,' 'equations,' 'quantities,' and 'target_variable' are left entirely undocumented, so an agent has no guidance on how to populate them correctly.

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 a specific verb and resource: 'Solve math word problems' and scopes it to the GSM8K/SVAMP/MATH benchmarks. This clearly differentiates it from sibling tools like cognitive.solve_arithmetic and cognitive.solve_equation_system, which target different problem types.

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 benchmark names imply the intended use case: textual math word problems from those datasets. However, the description does not explicitly state when to prefer this tool over alternatives, nor does it mention exclusions such as pure arithmetic or symbolic equation systems.

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