0to100

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

Annotations already mark it read-only, idempotent, and non-destructive. The description adds useful behavioral clarity: it returns ranked teams, probabilities, lean direction, and provenance. No side-effect or auth concerns are hidden.

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?

One dense, front-loaded sentence conveys the core object, scope, and return contents without filler. Everything is useful and no redundant schema detail is repeated.

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 both parameters are optional and the tool is read-only, the description supplies enough about the return payload and domain. It does not explicitly state what happens when a board name is omitted/invalid, but that is a minor gap for this simple getter.

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 the schema already explains both parameters. The description adds a little context by previewing provenance as show/quote/timestamp, but does not materially expand parameter meaning beyond what the schema documents.

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 names a specific resource ('Podcast-consensus futures boards') and a clear output (ranked teams with consensus probabilities and provenance). The e.g. 'Super Bowl winner' and mention of lean direction make it distinct from sibling tools like get_live_odds or get_roster.

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 wording implies this is for podcast-derived futures consensus, but it never explicitly says when to choose this over siblings like get_live_odds or get_tips. No exclusions or alternative routing are given.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds only the word 'Current' to convey freshness/temporal scope. No further behavioral detail (e.g., whether historical reports are available or how recent the data is) is disclosed, but the annotations carry the main burden.

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, concise sentence with no redundancy or filler. It immediately states the resource and scope, earning 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?

Given the tool is simple (one parameter, read-only annotations), the description is minimally adequate: it names the resource and the parameter's role. However, without an output schema or any detail about the report's structure, and no link to sibling list_teams for valid team values, an agent may not know how to correctly invoke it or interpret the 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?

Schema description coverage is 0%, and the only parameter 'team' has no description in the schema. The phrase 'for a team' clarifies that the team parameter refers to the team of interest, but it doesn't specify the expected format (name, abbreviation, ID), valid values, or how to obtain team identifiers. This is minimal compensation for the schema gap.

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 states a clear verb-implied action ('Current injury report') and a specific resource ('for a team'), making it unambiguous what the tool returns. It is distinct from sibling tools like get_roster or get_player_stats, as an injury report is a different data category.

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 usage is implied: an agent should call this when it needs current injury information for a team. However, no explicit alternatives, exclusions, or conditions are provided. It doesn't mention that team identifiers might come from list_teams, so the guidance is minimal.

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?

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context: scope is limited to the next game, the markets offered, the $1-payout math convention, and league availability. It doesn't mention update frequency or out-of-season behavior, but this is acceptable given annotation coverage.

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 compact, information-dense sentence with no filler. The core purpose is front-loaded, and the parenthetical adds valuable league availability context without diluting the main message.

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 a single parameter, safety annotations, and no output schema, the description does enough to guide selection and invocation: it names the resource scope, market types, payout convention, and league availability. It could be more explicit about return formatting and empty/error states, but the main payload is already sketched.

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 must define the 'team' parameter, and it does clarify that team refers to the team whose next game odds are returned. However, it doesn't specify whether team should be a name, abbreviation, or ID, nor does it point to list_teams for valid values, leaving some ambiguity.

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 states exactly what the tool returns: live betting markets (moneyline, spread, total) for a team's next game. This resource domain is clearly distinct from all sibling tools, which cover injuries, stats, rosters, schedules, and team lists.

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 clear context for when to use the tool: when live odds for a team's upcoming game are needed. It also adds league availability timing ('NFL live now; NBA opens with the season'). It does not explicitly name alternatives, but the sibling tools are in unrelated domains, so the intended use 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds that results are season aggregates with per-league stat categories, which is useful but overlaps with purpose; it does not disclose side effects, auth requirements, or edge-case behavior. No contradiction with annotations.

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?

Single sentence, front-loaded with the core scope, followed by compact parenthetical examples. Every word contributes; no filler or repetition.

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 simple, read-only, two-parameter lookup, the description plus annotations cover purpose and safety adequately. The only notable gap is an explicit team-format example; output details are lightly sketched through the stat categories, which is acceptable given no output schema and low complexity.

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 description coverage is 50%: player has an example, but team has none. The description clarifies that stats are for one player on a team and lists sport-specific stat types, but it does not specify what team values look like (e.g., city vs franchise name) or otherwise compensate for the bare team parameter.

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?

States a specific resource and scope: season stats for one player on a team, with sport-specific stat categories. This clearly differentiates it from sibling tools like get_roster (players) and get_team_schedule (games), and the title verb 'get' is supported by the descriptive noun phrase.

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 implies use for player stat lookups and indicates season-level data, but it never says when not to use it or names an alternative such as get_roster or list_teams. No explicit routing or exclusions are provided.

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?

Annotations already disclose read-only, idempotent, non-destructive behavior. The description adds useful output-organization details (grouped by position, jersey numbers) but no further behavioral traits such as data source or response size.

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?

A single, front-loaded sentence that states the output shape and its key attribute (jersey numbers) with 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?

For a simple read-only tool with one obvious required parameter and annotations covering safety, the description plus schema are sufficient to invoke it. It describes the return grouping and fields; only the lack of parameter format guidance prevents a higher score.

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 schema has 0% description coverage for the required 'team' parameter, and the description does not explain accepted formats, whether it is a team name or ID, or how to obtain valid values. The parameter is self-evident in context, but the description does not compensate for the missing schema documentation.

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 returns a full roster grouped by position with jersey numbers, and the name/title supply the get/resource framing. It does not explicitly contrast with sibling tools, but the roster resource is distinct from injury reports, odds, player stats, schedule, and team lists.

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?

No when-to-use guidance or alternatives are provided; the only signal is the name get_roster. An agent must infer that this is for roster queries and is not told how to choose among the sibling tools.

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?

Annotations already cover the read-only, idempotent, non-destructive nature of the tool, so the description's burden is lower. It adds useful context about the returned games' attributes (dates, opponent, venue), but offers no details on ordering, time range, pagination, or behavior for unknown teams.

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 front-loaded sentence with no wasted words, clearly conveying the resource and the key returned fields. It is appropriately concise.

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 simple one-parameter, read-only schedule lookup, the description covers the essential output shape and is supported by clear annotations. The main gaps are minor—ordering, timezone, or exact date range of 'upcoming'—but the tool is simple enough that the description is adequate.

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 schema already documents the 'team' parameter thoroughly with examples of accepted formats, so schema coverage is 100%. The description adds no additional parameter-level semantics beyond referring to 'a team.'

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 title and description make the tool's purpose obvious: retrieve upcoming games for a team, listing dates, opponent, and venue. It is clearly distinguishable from the sibling tools, which cover injuries, odds, player stats, rosters, and team lists.

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 implies this tool is for upcoming schedule information, but it does not explicitly state when to use it over alternatives or what it excludes, such as past games or full season schedules. Usage context is clear but not fully specified.

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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful context: the data is from the podcast-inference pipeline, is the 'latest', and supports filtering, which informs ordering and scoping expectations. No contradiction with annotations.

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 two compact sentences with zero filler. The core purpose and return fields are front-loaded, followed immediately by the filter options, making it efficient for an agent to process.

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 read-only listing tool with three fully documented optional parameters and strong annotations, the description adequately covers source, output fields, and filtering. There is no output schema, but the description lists the key fields. Minor gaps remain around the meaning of 'lean' and 'line context' and the default limit, but the schema handles the limit.

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 description coverage is 100%, and the description only restates the team and tip_type filters without adding syntax or format details. It does mention output fields like 'probability' and 'lean', but those are return-value attributes rather than parameter semantics, so it does not elevate the score above baseline.

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?

States a specific resource (latest 0to100 betting tips), the source pipeline (podcast-inference), and the returned attributes (pick, probability, lean, line context), plus filters. It is clearly distinct from most sibling tools (injury, roster, odds), though it does not explicitly differentiate from get_consensus_board even though 'consensus board' appears as a tip_type example.

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 clear context: use this tool to retrieve recent betting tips, optionally narrowed by team or tip type. The tip_type examples hint at subcategories like 'single-game sample' and 'consensus board', which helps an agent choose, but it stops short of explicit when-not-to-use guidance or naming 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?

Annotations already declare the tool read-only and idempotent, so the description is not burdened with safety disclosure. It adds useful behavioral context by specifying the exact scope (all 62 teams) and the output includes hub URLs, which goes beyond the annotations.

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, front-loaded sentence with no filler. Every element—scope, exact count, leagues, and output type—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?

For a simple list tool with no output schema, the description adequately communicates scope and output (teams with hub URLs), and the schema covers the optional filter. It could be slightly more complete by describing the return shape (e.g., field names) or explicitly mentioning the filter, but it is sufficient for an agent to call correctly.

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 100% description coverage and clearly documents the optional sport enum with 'Optional league filter'. The description reinforces the league breakdown but does not add new parameter syntax or semantics beyond what the schema already provides.

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 action ('List') and the resource ('all 62 pro teams') with an exact breakdown (32 NFL + 30 NBA) and what is included (hub URLs). It does not explicitly distinguish itself from sibling tools, but the resource and output are specific enough to avoid confusion.

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 implies this tool is for retrieving the full team list with hub URLs, but it provides no explicit guidance on when to choose it over alternatives or when not to use it. There are no exclusions or sibling comparisons, though the intended use is fairly obvious from the wording.

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