add

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

With no annotations, the description carries the full burden and does disclose the operation and its output shape ('return {"sum": a + b}'). For a stateless pure computation that is nearly the whole behavioral picture; it omits only edge-case behavior such as float precision or overflow.

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 operation before the usage note. The explicit return shape is mildly redundant given an output schema exists, but it costs little and reads cleanly.

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?

A 2-parameter, side-effect-free tool with an output schema needs little more than this; the return value need not be re-explained. Completeness would be perfect with a one-line note on precision or input domain (e.g., decimals vs integers).

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% (params a and b have only titles), so the description must compensate, and 'a + b' does map both parameters to their role as numeric addends. It doesn't restate types, which the schema already carries via "type": "number".

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 ('Add two numbers') and the exact result, with zero ambiguity. The sibling tools (lookup, store) are unrelated, so no differentiation is needed and none is possible to confuse.

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?

"Use for arithmetic the model should not do mentally" gives a clear condition for selecting this tool over doing the work inline. It stops short of explicit exclusions (e.g., large integers, floating-point precision caveats), but the intent is unmistakable.

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