pandascore_match_odds

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

The annotations already declare readOnlyHint, openWorldHint, and idempotentHint, which cover safety and mutation traits. The description adds valuable behavioral context: potential 403 on free keys, the return shape being approximate and unverified, and advice to inspect the live payload. This goes well beyond the annotations and prepares the agent for real-world failure modes.

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 longer than strictly necessary but each sentence provides essential information: purpose, return shape, reliability caveats, and an example. The structure is front-loaded with the core purpose and then handles important caveats. Slight verbosity in the caveat section is justified given the unverified nature of the data.

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?

With no output schema, the description carries the burden of explaining the return value, which it does by providing an approximate shape. It also covers authentication, potential 403s, and the need to verify field names. For a single-parameter tool, this is fairly complete, though it could specify how to identify the correct match ID from pandascore_matches more explicitly.

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?

The input schema has one parameter (matchId) with a clear description, giving 100% schema coverage. The description adds a concrete example and references pandascore_matches, which reinforces the schema but does not introduce novel semantic meaning. Baseline 3 is appropriate since the schema does the heavy lifting.

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 identifies the tool's function: retrieving betting markets and prices for a single esports match. It uses a specific verb-plus-resource construction and distinguishes itself from sibling opendota tools by noting it provides data 'the part `opendota` cannot give you.' This makes the purpose unambiguous.

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 gives useful context: it is the go-to for esports match odds when opendota data is insufficient. It also warns about the paid add-on and auth key requirements, implying when the tool may not work. However, it does not explicitly state alternatives or when not to use it beyond the opendota comparison, which is a slight gap.

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