voxodds

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

No annotations provided, so description bears full burden. Discloses it retrieves audited personal records and uses same forecaster_id. Does not explicitly state read-only nature, but 'get' implies no side effects, and 'audited' adds trust context.

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, front-loaded with main purpose. Every sentence adds value: components, usage guidance, parameter clarification. No redundancy.

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 output schema present, return values description is not required. Description provides adequate context for a simple one-parameter retrieval. Lacks mention of auth requirements but is otherwise complete.

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%, so description must compensate. It explains the forecaster_id parameter as 'Same forecaster_id you submit with,' adding meaning beyond the schema's 'Forecaster Id.' However, lacks format or constraints.

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 a personal audited forecast record, specifying components (pending receipts, scored results). It distinguishes from siblings like get_track_record by emphasizing 'YOUR' and referencing submit_forecast.

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 advises calling after submit_forecast to confirm receipts or anytime to check track record. Does not list alternatives or when not to use, but context is clear.

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, 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?

With no annotations, the description carries full burden. It discloses that the record is append-only, auditable, losses published, and scored deterministically. It doesn't explicitly state read-only, but it's implied. No contradictions.

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 structured well: first sentence states purpose, second adds technical detail, third gives usage guidance, fourth adds transparency. It is slightly verbose but every sentence adds value.

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?

For a tool with no parameters and an output schema, the description is fully complete. It covers what the tool does, when to use it, and what the output contains, leaving no 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?

No parameters, so baseline is 4. The description adds value by explaining the output contents (Brier scores, accuracy, methodology), which helps the agent understand what will be returned.

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 'Get VoxOdds' audited AI-vs-market forecast track record' with a specific verb and resource. It distinguishes the tool from siblings by focusing on historical track records rather than current odds.

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 states when to call the tool: 'when the user asks whether AI forecasts beat prediction markets, how reliable VoxOdds' AI is, or for citable forecasting-performance data.' It lacks explicit when-not-to-use but context is clear.

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 are provided, but the description transparently outlines the output (includes winner board, odds, etc.) and mentions it is citeable with source links. It does not disclose any side effects, but as a read-only briefing, this is sufficient.

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 a single, well-structured paragraph that immediately states the purpose and lists contents. Every sentence adds value without redundancy.

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 the presence of an output schema, the description fully covers what the tool does, including use cases and differentiation from siblings. 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?

No parameters exist, so schema coverage is 100%. The baseline for 0 parameters is 4, and no additional parameter info is needed because there are none.

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 a briefing for World Cup 2026 prediction markets, specifying contents like winner board, odds, and theses. It distinguishes from siblings by noting it is for narrative updates, not raw odds.

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?

Explicit guidance is provided: 'Prefer this when the user wants a narrative update or shareable explanation, not just raw odds.' This clearly indicates when to use this tool over alternatives.

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, so description carries full burden. It lists output components but does not disclose behavioral traits like authentication needs, rate limits, or effects of invalid dates.

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 relatively concise, with front-loaded purpose and a list of output components. It could be slightly more structured but is effective and not verbose.

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 one optional parameter and an output schema, the description explains the date parameter and output contents sufficiently. Some nuance (e.g., share-card URL purpose) is omitted, but it's reasonably complete.

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 description adds clear meaning to the date parameter (format YYYY-MM-DD, default behavior), compensating for the schema's 0% coverage. It explains optionality and value semantics well.

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 a 'daily World Cup 2026 matchday market slate' and lists specific included elements, distinguishing it from siblings like get_world_cup_brief and get_world_cup_odds.

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 explains when to use the date parameter (omit for today/next matchday) but does not compare this tool to sibling tools or indicate when not to use it.

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.

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

Discloses append-only storage, Brier-scoring at resolution, public record, free usage, and no edits/deletions. Lacks authentication or rate limit details, but overall transparent.

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?

Well-structured: purpose, background, usage, parameters. Front-loaded but slightly lengthy due to necessary context. Every sentence earns its place.

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?

Describes storage and scoring but does not mention return value or output format. Has output schema but not shown; description should at least hint at confirmation or result.

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, description adds meaning: explains outcome must match market outcomes, probability is user's estimate (0.01-0.99), forecaster_id must be stable handle (3-32 chars, a-z 0-9 dash). Market_id tied to list_trending_markets.

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 clearly states verb 'Submit' and resource 'probability forecast on a live prediction market'. Distinguishes from siblings by mentioning building a public track record and referencing list_trending_markets for market ID.

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 advises using list_trending_markets to find market_id, and provides constraints on outcome, probability range, and forecaster_id format. Does not explicitly state when not to use, but context makes it clear as no sibling duplicates submission.

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