math_fibonacci

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

With no annotations, the description carries the full behavioral burden, and it does disclose one meaningful trait: an error is returned for negative n. However, it says nothing about overflow for large n, performance, or the accepted integer range, so the disclosure is partial rather than complete.

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 short sentences, front-loaded with the core behavior and adding only the indexing convention and error case. 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?

For a single-parameter math function with an output schema already documenting the return value, the description covers purpose, indexing, and failure mode. Only the numeric range/overflow behavior is left implicit.

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 0% for the single parameter, so the description must compensate; it does so by establishing that n is the index, that indexing starts at 0, and that negative values are invalid. That is solid semantic grounding, though it does not state an upper bound on n.

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?

States a specific verb and resource ('Return the nth Fibonacci number') and pins down the indexing convention with '(0-indexed)'. This cleanly separates it from the neighboring math_factorial and math_power tools without needing to open any schema.

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?

No guidance on when to choose this over math_factorial or other math siblings, and no stated preconditions beyond the error case. The negative-input note is a boundary, not usage routing, so the agent gets no when-to-use context.

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