footdigest

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

With no annotations, the description fully carries behavioral disclosure. It lists returned fields (grouped matches, scores, winners), implying read-only behavior. It does not mention authentication or rate limits, but the scope is clear and non-destructive.

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 sentence that front-loads the core purpose. The inclusion of 'Footdigest:' adds slight brand context but does not harm clarity. Efficient with no wasted words.

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's simplicity (one parameter, no output schema), the description adequately specifies the input (competition slug) and output (bracket rounds with teams, scores, etc.). It covers all essential aspects for an agent to invoke 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?

Schema coverage is 100% as the 'competition' parameter is described with an example slug. The description adds minimal value ('Give the competition slug'), essentially paraphrasing the schema.

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 retrieves a competition's knockout bracket with matches grouped by round, including teams, scores, shootouts, status, and winners. This distinctively separates it from sibling tools like get_match or get_schedule.

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 usage by instructing to 'Give the competition slug,' but it does not explicitly state when to use this tool over alternatives (e.g., for complete bracket data) or 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?

With no annotations, the description carries full behavioral burden. It discloses sorting ('most consequential first') and inclusion of 'group table crossings', but lacks details on authentication, rate limits, return format, or pagination. Basic 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?

The description is two sentences and covers the essentials without excessive verbosity. However, the first sentence is slightly wordy ('Footdigest:...') and could be more streamlined.

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 simple parameters and no output schema, the description provides adequate context for the tool's purpose and input. However, it does not describe the output structure, and more detail on how this complements sibling tools would improve completeness.

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% and already describes both parameters. The description adds minimal value: it restates 'optional limit' and 'competition slug'. No new semantics are provided beyond schema, so baseline score of 3 is appropriate.

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 defines the tool as retrieving a competition's defining moments, sorted by consequence, and including group table crossings. It uses specific verbs and resource, distinguishing it from sibling tools like 'get_standings' or 'get_qualification_scenarios'.

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 usage by saying 'Give the competition slug' and mentions an optional limit, but does not explicitly state when to use this tool versus alternatives. No when-not or exclusion criteria 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?

With no annotations provided, the description adequately discloses the behavioral traits: it returns wins, draws, goals, recent meetings, and penalty shootouts. It does not mention side effects or authentication, but for a read-only tool 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 sentence, front-loaded with the main purpose. The inclusion of 'Footdigest' is slightly extraneous but does not harm conciseness. Generally efficient.

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 covers the main outputs but lacks details on return format (e.g., list vs aggregated stats) and error handling. Given the simplicity (3 params, no output schema), it is adequate but not 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 description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond the schema; it only states 'Ask by team names'. The parameters are fully described in the schema, so no penalty.

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 retrieves the all-time head-to-head record between two men's national teams, specifying verb 'get' and resource 'head-to-head record'. Although sibling tools exist, the purpose is distinct and 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 implies usage by saying 'Ask by team names', but does not explicitly state when to use this tool versus alternatives like 'get_match' or 'get_standings'. No when-not or alternative guidance is 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?

No annotations are provided, so the description must disclose behavioral traits. It lists the data returned (teams, status, etc.), which is helpful, but does not mention idempotency, rate limits, or side effects. For a read-only tool, this is adequate but not thorough.

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 sentence that front-loads the source and purpose, efficiently listing all output fields. No redundant words.

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 simple input (one parameter) and no output schema, the description provides sufficient context: input source, output contents. It could mention if it's read-only, but overall completeness is high.

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% with one parameter 'match_id' described as 'Fixture id, e.g. from get_schedule.' The description repeats this hint slightly, adding marginal value beyond the schema. Baseline applies.

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 'one fixture in detail' and lists specific fields (teams, status, kickoff, score, stage, venue, event timeline). It also names the source 'Footdigest', making the purpose concrete and distinguishable from siblings like get_match_brief and get_schedule.

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 how to use it: 'Ask by match_id (from get_schedule).' This provides clear context for when to invoke it (after obtaining a schedule) but does not explicitly exclude scenarios or mention 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?

With no annotations provided, the description must disclose behavioral traits but only describes the returned content. It does not state side effects, auth requirements, rate limits, or confirm read-only nature, leaving a significant gap in transparency.

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 efficient sentence of 20 words, front-loading the core purpose ('Footdigest: the AI-generated brief for a match') and covering parameters succinctly 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?

With no output schema, the description partially explains return values (pre-match brief, recap, impacts) but lacks detail on structure or format. It adequately covers the tool's function for two parameters, but could be more explicit about output type.

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 minimal value beyond the 100% schema coverage by rephrasing parameter usage ('Ask by match_id; optional locale'), but does not provide deeper semantic context or examples, meeting the baseline for high coverage.

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 an AI-generated brief for a match, specifying content types (pre-match five-lens brief, post-match recap, event impacts) and source (Footdigest's engine), effectively distinguishing it from sibling tools that provide other match-related data.

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 indicates usage by 'Ask by match_id' and notes optional locale, implying when to use, but provides no guidance on when not to use or alternatives among sibling tools like get_match.

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 discloses key traits: 'backtested Dixon-Coles + Elo engine', 'every answer is sourced and timestamped', and references methodology. It doesn't cover all behaviors (e.g., optional parameters), but the core behavioral context is well provided.

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 tightly written sentences. The first sentence states the main purpose and engine; the second covers usage and supplementary references. No wasted words.

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 no output schema, the description effectively summarizes the return: probabilities and expected goals. It references get_model_card for deeper methodology. For a focused data tool, this is sufficient, though a bit more detail on output format would improve completeness.

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% (baseline 3). The description adds value by explaining the alternative calling modes (match_id vs. team names), which helps agents understand how to construct valid requests beyond 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 the tool provides 'calibrated home/draw/away probabilities and expected goals for a football matchup', using a specific verb and resource. It distinguishes from siblings (e.g., get_match, get_head_to_head) by focusing on probabilities and expected goals.

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 specifies calling by match_id or by home_team/away_team names, and directs to get_model_card for methodology. While it doesn't explicitly state when not to use this tool, the context of probabilities vs. other match data 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?

With no annotations, the description carries full burden. It discloses what the tool returns and even includes what is not measured. It does not mention side effects, but as a read-only data retrieval operation, the description is sufficiently 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?

Two sentences, front-loaded with key purpose, lists returned items efficiently, no wasted words.

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 no output schema, the description provides a complete overview of the tool's functionality. It could optionally hint at return format, but is sufficient for selection and invocation.

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 and schema coverage is 100%, so the description does not need to add parameter information. Baseline 4 is appropriate.

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 'live model card' with specific components (RPS, OOS size, calibration table, methodology, list of what is not measured) and its purpose for auditing predictions. This distinguishes it from sibling tools like get_match or get_standings.

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 'Use it to audit any number the other tools return', providing clear when-to-use guidance. However, it does not mention when not to use or alternative 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?

No annotations provided, so description bears full burden. It discloses seeded and reproducible behavior, but does not mention simulation nature or limitations like only working for groups. Adds some value 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?

Two sentences, front-loaded with key information, includes an illustrative example. No fluff.

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 without output schema, description explains what it returns (qualification chances and scenario effects) but lacks explicit output format. Sufficient for an agent to understand the tool's value.

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 covers 100% of parameters with descriptions. The overall description adds little beyond schema, just instructing to provide competition and team. Baseline of 3 is appropriate.

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 the tool computes a team's qualification chances from its group and how remaining results affect it. This distinguishes it from siblings like 'get_tournament_odds' or 'simulate' which are more general.

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?

Description instructs to provide competition slug and team name, but does not explicitly exclude alternatives or state when not to use this tool. Implicitly clear but lacks comparative guidance.

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, so the description carries the burden. It states what the tool returns (fixtures with attributes) but does not disclose if it is read-only, error handling, or side effects. The description implies a read operation but lacks explicit safety 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?

Two sentences with no wasted words. First sentence defines functionality, second explains usage. Information is front-loaded and easy to parse.

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 no output schema, the description lists key return attributes (teams, kickoff time, status, score, stage), which is adequate. It covers the main inputs and outputs. Could mention read-only nature, but overall complete for a simple list 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?

Schema has 100% coverage with descriptions. The description adds value by providing examples for status ('scheduled, live, finished') and clarifying limit as a cap. This enhances understanding beyond the schema's default/max info.

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 fixtures for a competition with teams, time, etc. It distinguishes from siblings like get_match (single match) and get_standings, but does not explicitly differentiate. The verb 'get' is implied and resource is a competition schedule.

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 instructions on how to use: give competition slug, optionally filter by status and limit. However, it does not mention when not to use (e.g., for a single match use get_match) or alternatives among siblings.

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 burden. It indicates the tool provides 'current' standings, implying real-time data. It does not mention read-only nature or side effects, but for a query tool the behavior is adequately implied.

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 that includes an example, front-loading the purpose. Every word 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?

Given no output schema, the description explains the return values (points, goal difference, position, qualification status). It covers the essential information needed to understand the tool's output without missing details.

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%, so baseline is 3. The description adds value by providing concrete examples for both parameters (e.g., 'world-cup-2026' for competition and 'A' for group), which aids the agent in correct invocation.

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 group standings including points, goal difference, position, and qualification status. It distinguishes the tool from siblings by specifying 'standings' rather than brackets or matches, but does not explicitly differentiate from other tools.

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 the tool is used for fetching standings by providing a competition slug and optional group code, but does not explicitly state when to use this tool over alternatives like get_bracket or get_match. No when-not guidance.

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, so the description carries the full burden. It mentions 'per the competition's fair-play rules', hinting at rule-based logic, but does not disclose whether the tool is read-only, any authentication needs, or potential side effects. Behavior is essentially defined by the output (list of players), but not explicitly.

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 very concise, consisting of two sentences. The first sentence conveys the purpose, and the second instructs on input. However, the 'Footdigest:' prefix adds minimal value. Still, it is well-structured and front-loaded.

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 simple parameter and no output schema, the description leaves out important context such as return format (list of player names? team info?), time relevance, or any additional output details. It feels incomplete for a full understanding of the tool's usage.

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% with a clear description of the 'competition' parameter. The description repeats 'Give the competition slug' without adding new meaning. Baseline 3 is appropriate since the schema already documents the parameter adequately.

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 provides information about who is suspended for the next match and who is one booking away from a ban, using competition fair-play rules. The verb 'get' is implied, and the resource is suspension watch, which distinguishes it from siblings like get_standings or get_match.

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 usage when needing suspension info for a competition, but it does not explicitly state when to use this tool instead of alternatives like get_team_availability or get_match_brief. No guidance on 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?

The description discloses that it returns active injuries with specific fields, but with no annotations, it misses details like read-only nature, authorization requirements, or error handling. It provides basic behavioral context but not comprehensive transparency.

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 sentence with a clear instruction, making it concise and front-loaded. The prefix 'Footdigest:' is slightly unusual but doesn't detract from clarity.

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 simple input schema (2 required parameters) and no output schema, the description adequately covers the tool's purpose and input requirements. It could mention return formats or limitations, but for a straightforward tool it is sufficiently 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 100%, so baseline is 3. The description adds value by specifying that 'team' is a team name (e.g., France) and 'competition' is a slug (e.g., world-cup-2026), clarifying format beyond the schema descriptions.

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 retrieves a team's active injuries, listing specific details like player, type, status, and expected return. This verb+resource combination is distinct from siblings, which cover brackets, matches, standings, etc., so differentiation is clear.

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 instructs to provide 'the competition slug and a team name', which is helpful for parameter usage. However, it does not explicitly state when to use this tool vs alternatives or provide any exclusions for team/competition validity.

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 mentions Monte Carlo simulation (indicating randomness), seed for reproducibility, and trials cap at 50000. Missing details like computational cost or that it is read-only, but the key behavioral aspect is covered.

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, concise and front-loaded. The first sentence conveys purpose, the second explains usage. Minor noise with 'Footdigest:' but overall efficient.

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?

No output schema, so description should clarify result shape. It says 'each team's chances' but not format (list, dict). With many sibling tools, not explicitly differentiated. Adequate but leaves gaps on output structure and alternative contexts.

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 3. The description adds 'control and reproduce' context but does not provide meaning beyond schema descriptions. Slight value in emphasizing parameter roles, but no new semantic detail.

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 provides each team's chances for qualifying from group stage, reaching final, and winning the competition via a Monte Carlo simulation. It specifies the source (Footdigest) and mentions strength-awareness, which distinguishes it from siblings like get_match_probabilities or simulate.

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 explains how to use the tool: pass trials and seed for control/reproducibility, identify competition by slug. However, it does not explicitly state when not to use it or contrast with alternatives like get_match_probabilities, though the purpose is specific enough for inference.

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, so the description must fully disclose behavior. It states the simulation is deterministic with same inputs and mentions the Monte Carlo method. However, it omits details such as error handling (e.g., what if competition slug is invalid), required permissions, or rate limits. The performance cap (50000 trials) is in the schema but not reiterated. Overall 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?

The description is three sentences: first for purpose, second for input control details, third for identifier example. Every sentence serves a purpose with no wasted words. It is front-loaded and easy to parse.

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 three parameters, 100% schema coverage, and no output schema, the description explains the core functionality and parameter usage. However, it does not detail the return format (e.g., object mapping team to probability) or edge cases. Slightly incomplete for a simulation tool that could have complex output, but adequate for typical use.

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%, providing baseline 3. The description adds value by explaining that the same inputs always return the same numbers (reproducibility), which is not in the schema. It also contextualizes the seed and trials as control knobs. However, it does not introduce new details beyond what the schema already provides for each 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?

The description clearly states the tool runs a seeded Monte Carlo simulation over a competition's group stage and returns each team's advancement probabilities. It uses a specific verb ('run') and resource ('Monte Carlo over group stage'), and distinguishes itself from siblings like get_match_probabilities (match-level) and get_tournament_odds (possibly different format).

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 explains that trials and seed control the run and ensure reproducibility, and that the competition is identified by slug. While it doesn't explicitly exclude alternatives, the context is clear enough for an agent to know when to use this tool (for group stage advancement probabilities) versus siblings like get_qualification_scenarios or get_standings.

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