voxodds

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

No annotations provided, description covers output ('implied probabilities and source links') but not edge cases (e.g., no match found), rate limits, or read-only confirmation. Adequate but incomplete.

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 sentences with example, no redundancy. Front-loaded with purpose and usage. Each sentence adds meaningful information.

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?

Output schema exists, so description need not detail return format. Coverage of usage and output is good for a simple tool. Minor gap: no mention of error or no-match handling.

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 0% but description adds value: explains parameter is a natural language question and provides example. Enhances understanding beyond schema type 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?

Clear verb+resource: 'Get live prediction-market odds' for real-world events with natural language questions. Distinguished from sibling 'get_world_cup_odds' by scope (general vs. specific).

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?

Explicitly states when to call: when user asks about probability/odds/likelihood of any future event, with examples. Lacks explicit exclusion or mention of sibling alternative for World Cup-specific queries.

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

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

With no annotations, the description carries full burden. It discloses the tool's output structure (thesis, entry logic, invalidation criteria, live price tracking) and includes the caveat 'Research framing only - not financial advice.' This adequately conveys behavioral traits for a read-only tool.

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 three sentences, front-loaded with the tool's purpose, followed by usage guidance and a disclaimer. Every sentence adds value, and there is no redundancy or verbosity.

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 tool has no parameters and an output schema exists, the description fully explains the output's content. It covers all necessary context for a simple retrieval tool, making it complete for the agent's needs.

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 tool has zero parameters, so baseline is 4 per guidelines. Schema coverage is 100% (vacuous), and the description does not need to add parameter info. The description adds value by explaining what the output contains, which is sufficient.

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 retrieves 'VoxOdds research desk theses' and details their content (mispriced markets, thesis, entry logic, etc.). It distinguishes from sibling tools (e.g., get_market_odds for raw odds, list_trending_markets for trends) by focusing on research 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 description explicitly says 'Call this when the user asks where the value is, what to research, or for prediction-market trade ideas,' providing clear usage context. It lacks explicit when-not-to-use guidance or alternative tool mentions, but the given context is strong.

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

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

No annotations provided, but the description fully covers behavior: it is a read-only query, updated every 10 minutes from prediction markets, with no destructive effects. The source and update frequency are disclosed.

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 concise sentences front-load the action and list odds types, usage context, and update frequency. No superfluous text.

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 zero parameters and an output schema, the description provides all necessary context: what odds are retrieved, when to use, and data freshness. It is complete for the tool's simplicity.

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?

Zero parameters, baseline 4. The description adds no parameter details since none exist, but it correctly implies no inputs are needed.

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 gets live World Cup 2026 odds, specifies the types of odds (tournament winner, group winners, etc.), and distinguishes from siblings by being World Cup-specific. The verb and resource are explicit.

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 tells when to call: 'for any question about World Cup 2026 favorites, teams, groups, or matches.' It also provides context about the date range and sibling tools like get_market_odds serve broader needs.

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

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

Without annotations, the description carries the full burden. It discloses ordering by volume and use of filters, but omits potential behavior like pagination, data freshness, or any side effects. Adequate but not comprehensive.

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 concise sentences, front-loaded with purpose, followed by usage and parameter details. No extraneous information.

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 presence of an output schema, the description focuses appropriately on inputs and purpose. It could add details on limit behavior or pagination, but overall it's sufficient for a listing tool.

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 coverage, the description compensates well by explaining `category` (listing allowed values) and `search` (keyword filter). However, `limit` is not described, leaving a gap despite the default value.

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?

Description explicitly states the action ('List live prediction markets ordered by 24h volume') and the resource ('prediction markets'), distinguishing it from sibling tools that focus on individual markets or theses.

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?

Provides context on when to use ('to browse what the market is pricing right now or to find a specific market') but does not explicitly mention when not to use or contrast with sibling tools like get_market_odds.

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