solve_constraint_satisfaction

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. It mentions using the Z3 SMT solver and a timeout parameter, but does not disclose behavioral traits like side effects, error handling, or whether it modifies state. The description adds minimal behavioral context beyond basic usage.

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 well-structured with a summary, usage statement, parameter list, return description, and example. It is front-loaded with the core purpose. Minor verbosity in the 'ideal for' sentence could be trimmed, but overall it is efficient and clear.

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 includes a helpful example and parameter explanations, but lacks details on return value format (e.g., sat/unsat, variable values) and does not address edge cases or error behavior. Given the tool's complexity and absence of output schema and annotations, more context would be beneficial.

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%, so the description compensates well by explaining each parameter in the 'Args' section and providing a concrete example. The explanation of 'variables' having 'name' and 'type' fields, and 'constraints' as expressions, adds meaning that the schema alone lacks.

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 solves constraint satisfaction problems using the Z3 SMT solver, and mentions its applicability to logical reasoning and puzzles. However, it does not explicitly differentiate from sibling tools like solve_constraint_programming or solve_linear_programming, leaving some ambiguity about when to choose this specific solver.

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 says the tool is 'ideal for' certain problem types but provides no guidance on when not to use it or how it compares to sibling tools (e.g., constraint programming, convex optimization). Without explicit exclusions or alternatives, an AI agent lacks the context to choose correctly.

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