Sports Trading Card Agent

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

With no annotations provided, the description carries the full disclosure burden. It successfully explains the data inputs (market trends, player performance) and output format (recommendation with confidence level, supporting data). However, it omits operational details like data freshness, calculation methodology, or rate limits that would help agents understand behavioral constraints.

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 docstring-style structure (description + Args + Returns) is well-organized and front-loaded with the core purpose. Every section is necessary given the 0% schema coverage. The Returns section risks redundancy with the existing output schema, though without seeing that schema's content, its inclusion is justified.

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 the 3-parameter complexity with zero schema coverage, the Args section provides necessary completeness. The presence of an output schema means the Returns section is optional but not harmful. Missing only explicit sibling differentiation to be fully complete for an agent selecting between multiple card analysis tools.

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?

With 0% schema description coverage, the description fully compensates through the Args section. It provides clear semantic meaning for each parameter: card_query includes specific format examples, player_name explains the cross-reference purpose, and sport enumerates valid values (nba/nfl/mlb) with defaults. This adds critical meaning absent from the bare schema.

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 provides 'buy/sell/hold recommendations' for sports cards using market trends and player data. The specific advisory nature (BUY/SELL/HOLD/AVOID) distinguishes it from sibling lookup tools like card_price_lookup or player_stats_lookup, though it could explicitly clarify when to choose this over card_market_analysis.

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 Args section provides excellent parameter-level guidance (e.g., player_name 'improves accuracy', sport defaults to 'nba'), specifying required vs optional inputs. However, it lacks explicit guidance on when to use this investment advisor versus siblings like card_market_analysis or vintage_card_analysis.

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