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

With no annotations, the description adds value by noting the probabilities are 'calibrated', 'backtested', and 'sourced and timestamped'. This gives insight into the tool's reliability beyond what the name or schema convey. It does not address auth, rate limits, or potential side effects.

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?

Three sentences, no wasted words. Each sentence serves a purpose: stating output/model, query methods, and sourcing/reference. Front-loaded with the core function.

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 lacks output schema details (exact fields returned), but given the complex context and zero required parameters, it provides enough to understand usage. The reference to the model card fills some methodological gaps.

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 100%, so baseline is 3. The description adds value by clarifying the alternative query modes (match_id vs. team names) and implying that any matchup can be queried, which is not obvious from the schema alone.

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 returns 'home/draw/away probabilities and expected goals' for a football matchup, specifying the underlying models (Dixon-Coles + Elo). It distinguishes from siblings like 'get_match' which likely return match details rather than probabilities.

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?

It explicitly says to query 'by match_id, or by home_team and away_team names', providing clear usage context. However, it does not state when NOT to use this tool or mention alternatives beyond referencing 'get_model_card' for methodology.

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