search_expenses

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

No annotations are provided, so the description carries full burden. It mentions returns both matching expenses and a summary (total, count, avg), which adds transparency. However, it does not disclose potential side effects, permissions, or limits like pagination.

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 concise at a few lines with embedded examples. It is front-loaded with the key purpose. The examples add value without unnecessary repetition.

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?

Given 10 parameters with no schema descriptions and no annotations, the description is moderately complete for the primary use case but fails to explain the majority of parameters or edge cases. The return format is partially described.

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%, and the description does not explain any parameter individually. It assumes the query parameter handles natural language but ignores tags, year, month, etc. With 10 parameters, this is insufficient guidance.

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 it is for 'Natural language expense search' and provides multiple examples showing conversational queries. It distinguishes from sibling tools like list_expenses and get_expense by emphasizing natural language input.

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 explicitly says 'Claude should use this when the user asks conversational questions about their spending,' giving clear context. It does not mention when not to use or alternatives, but the sibling tools imply non-conversational search options.

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