footdigest

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

No annotations, but description fully discloses what the tool returns (rounds, teams, scores, etc.) and that it is a read operation. Does not mention side effects or permissions, acceptable for a simple get 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?

One sentence, no fluff except 'Footdigest:' branding which is unnecessary. Gets straight to the point, but could be shorter without brand.

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 output comprehensively (rounds, teams, scores, shootouts, status, winners). No output schema, so description suffices. Lacks error handling info, but fine for simple retrieval.

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% description coverage with an example. Description adds 'Give the competition slug', which reinforces but adds minimal new meaning. Baseline 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 it returns a competition's knockout bracket, grouped by round with match details. Uses specific verb 'get' and resource 'bracket'. Differentiates well from siblings like get_schedule 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?

Implies usage by stating 'Give the competition slug' but no explicit when/when-not or alternatives. Sibling tools provide context but description lacks direct 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 must carry full behavioral disclosure. It mentions ordering ('most consequential first') and inclusion of 'group table crossings', but fails to state whether the operation is read-only, what happens on invalid input, rate limits, or auth requirements. For a tool with no annotations, this is insufficient.

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 concise, comprising a single sentence that conveys the core purpose and key parameters. However, the opening 'Footdigest: ' adds unnecessary branding, and the sentence structure is somewhat awkward. Still, it is efficient with no redundant sentences.

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 2 simple parameters, no output schema, and no annotations, the description should compensate by explaining what the output looks like or what 'defining moments' entail. It does not describe the return format, pagination, or error behavior, leaving the agent with significant unknowns about the tool's behavior.

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?

Input schema coverage is 100%, with both parameters adequately described in the schema (limit: default 5, max 20; competition: slug example). The description merely restates these ('Give the competition slug; optional limit') without adding new meaning. Baseline 3 is appropriate as schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns a competition's defining moments that moved teams across qualification lines, ordered by consequence. It identifies the resource (defining moments) and action (get). While the phrasing 'Footdigest: ...' is somewhat awkward, it conveys the unique purpose, distinguishing it from siblings 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 provides no guidance on when to use this tool versus alternatives such as get_standings or get_qualification_scenarios. It does not specify prerequisites, when not to use it, or how to choose between siblings. This lack of usage context limits the agent's ability to select the tool appropriately.

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 carries full disclosure burden. It only states the data source ('historical results dataset') but does not mention data freshness, caching behavior, or any side effects. It does not clarify that the tool is read-only or if authentication is required.

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 front-loads the purpose. The brand prefix 'Footdigest:' is minor noise but does not detract significantly. Every word is relevant, making it 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 tool has no output schema and is relatively simple. The description covers the main outputs (wins, draws, goals, recent meetings, penalty shootouts) but does not specify the exact return format or field definitions. This is adequate but leaves some ambiguity.

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 reinforces that team1 and team2 are team names ('Ask by team names') but adds no new meaning beyond the schema descriptions. The limit parameter is not mentioned, so no extra 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?

The description clearly states the tool provides 'all-time head-to-head record' between two men's national teams, specifying included details like wins, draws, goals, recent meetings, and penalty shootouts. It distinguishes itself from sibling tools like get_match or get_standings by focusing on historical head-to-head 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 mentions 'Ask by team names,' implying the need to provide two team names. However, it lacks explicit guidance on when to use this tool versus alternatives, such as get_match for individual matches, or any prerequisites like exact name formats.

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 explains the output: teams, status, kickoff, score, stage, venue, and event timeline. No mention of side effects or auth, but for a read operation this is 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: first lists returned data, second gives parameter guidance. No wasted words, front-loaded with key info.

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 covers the main return fields. It doesn't address edge cases (e.g., unplayed match timeline), but status is mentioned so it's 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?

Schema coverage is 100%, so baseline 3. The description adds context that match_id comes from get_schedule, which is meaningful beyond 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 detailed info for one fixture, listing specific fields. It doesn't explicitly differentiate from sibling 'get_match_brief', but the detail level is implied.

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 mentions to query by match_id from get_schedule, giving context. It doesn't exclude alternatives or state when not to use.

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 full burden. It mentions 'AI-generated' and brief types but fails to disclose data freshness, error behavior, or output structure. Lacks crucial behavioral details for a retrieval 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?

Two sentences, front-loaded with the tool's purpose, followed by usage instruction. Every word earns its place 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?

With no output schema and simple parameters, the description adequately covers input but not output. Listing brief types is helpful, but agents may benefit from knowing the structure of the response. Adequate but not robust.

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 descriptions for both parameters. The description adds no new semantic meaning beyond confirming locale options. Baseline 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 states the tool retrieves AI-generated match briefs including pre-match, post-match, and event impacts. It distinguishes itself from sibling tools like get_match (match details) and get_head_to_head (head-to-head stats) by specifying the brief nature.

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 ask by match_id and optional locale, but does not explicitly state when to use this tool over siblings or provide exclusions.

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 adds value by noting the probabilities are 'calibrated', 'backtested', and 'sourced and timestamped'. This gives insight into the tool's reliability beyond what the name or schema convey. It does not address auth, rate limits, or potential side effects.

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, no wasted words. Each sentence serves a purpose: stating output/model, query methods, and sourcing/reference. Front-loaded with the core function.

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 lacks output schema details (exact fields returned), but given the complex context and zero required parameters, it provides enough to understand usage. The reference to the model card fills some methodological 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?

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying the alternative query modes (match_id vs. team names) and implying that any matchup can be queried, which is not obvious from 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 it returns 'home/draw/away probabilities and expected goals' for a football matchup, specifying the underlying models (Dixon-Coles + Elo). It distinguishes from siblings like 'get_match' which likely return match details rather than probabilities.

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 explicitly says to query 'by match_id, or by home_team and away_team names', providing clear usage context. However, it does not state when NOT to use this tool or mention alternatives beyond referencing 'get_model_card' for methodology.

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 the returned components and the audit purpose. It could add details about data freshness or prerequisites, but the provided information is sufficient 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?

Two concise sentences: first introduces the tool, second lists contents and usage. 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 and no parameters, the description adequately covers what the tool does and returns. Could mention if data is static or refreshed, but overall 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?

No parameters in input schema (100% coverage). Description adds value by explaining the output, meeting the baseline for zero-parameter tools.

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 vs baseline, out-of-sample size, calibration table, methodology, list of not measured). It distinguishes itself from sibling tools by focusing on auditing model predictions.

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 says 'Use it to audit any number the other tools return', providing a clear use case. No explicit exclusions, but the context of sibling tools makes the usage context 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 that results are seeded and reproducible, and that it shows how each result moves chances. However, it doesn't explicitly state that it runs simulations (though param descriptions cover trials), nor any safety aspects like read-only nature.

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 packed with purpose, behavior, and required inputs. No redundancy or waste. Front-loaded with core concept.

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 provides a good mental model of what is returned (how chances move per result). It mentions seeded/reproducible behavior but lacks explanation of trial count effect or output format specifics. Still, adequate for a simulation 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 covers all 4 parameters with descriptions. The description adds minimal extra meaning beyond summarizing inputs (competition slug and team name). Baseline 3 is appropriate given high schema 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 computes a team's qualification chances and how results affect them, specifying inputs (competition slug, team name) and distinguishing from siblings like get_standings or get_tournament_odds by focusing on scenario 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 tells the user to provide competition slug and team name. It implies use for group stage qualification but doesn't explicitly state when not to use or mention alternatives. Still, guidance is clear enough for most users.

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 cover behavioral aspects. It discloses the output fields and input constraints, but lacks details on error handling, rate limits, or data freshness. For a simple list 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?

Two clear sentences: first describes output, second explains inputs. No fluff, every word adds value, and the most important information is 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?

For a tool with 3 parameters and 100% schema coverage, the description covers the essential inputs and outputs. It could mention default ordering or pagination, but overall it is sufficiently 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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the competition parameter as a 'slug', giving examples for status filter (scheduled, live, finished), and confirming that limit caps results. This enhances comprehension beyond 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 the tool retrieves fixtures for a competition, listing included fields (teams, kickoff time, status, score, stage). It distinguishes itself from sibling tools like get_match (single match) or get_standings by focusing on the schedule of multiple matches.

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 use the tool: provide a competition slug and optionally filter by status and limit. It does not explicitly state when not to use or name alternatives, but the context of sibling tools implies the distinction.

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 must cover behavioral traits. It describes the tool as returning current standings, but does not explicitly state it is read-only, nor disclose any auth needs or rate limits. The 'current' implies no side effects, but could be more explicit.

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, no extraneous words. First sentence states purpose and output, second explains parameters. Efficient 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 no output schema and only 2 parameters, description covers inputs and outputs adequately. It could mention default behavior when no group is provided, but overall 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?

Schema coverage is 100%, and description adds examples for both parameters (e.g., 'world-cup-2026' for competition, 'A' for group), providing more context than 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?

Description clearly states it returns group standings including points, goal difference, position, and qualification status per team. It specifies the resource (competition standings) and distinguishes from sibling tools like get_bracket 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?

Description tells the agent to provide the competition slug and optionally a group code, implying when to use. However, it does not explicitly state when not to use or mention alternatives, but the context is sufficient for selection.

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 describes the tool's output (suspended players, those one booking away) but does not mention side effects, permissions, or read-only nature. For a read-only retrieval tool, this is minimally acceptable.

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 concise and to the point, using a single sentence to convey the tool's purpose and requirement. It lacks structure but is 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?

Given the low complexity (one parameter, no output schema), the description covers the main functionality. However, without an output schema, it would benefit from describing the response format or potential edge cases.

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 description for the only parameter. The description adds 'Give the competition slug,' which reinforces but does not add significant new meaning beyond the schema's example.

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 suspension watch information, including who is suspended and who is one booking away, per competition fair-play rules. It distinguishes itself from sibling tools like get_standings 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 instructs the user to provide the competition slug, but does not explicitly list when to use this tool versus alternatives or mention exclusions. The context is clear enough for the intended use case.

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, and the description only implies a read operation without disclosing permissions, side effects, or limitations.

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 with all necessary information, though the 'Footdigest:' prefix is unnecessary and slightly detracts from conciseness.

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?

Lists returned fields but lacks details on response structure or pagination; adequate for a simple two-parameter query 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 coverage is 100% with clear parameter descriptions; the description adds minimal clarification (e.g., competition slug format).

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 data fields. It distinguishes from siblings like get_suspension_watch and 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?

Indicates when to use with 'Give the competition slug and a team name', but lacks explicit exclusions or comparisons to 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 are provided, so the description carries the full burden. It discloses the Monte Carlo method, strength-awareness, reproducibility via seed, and simulation count with a cap at 50000. This is good behavioral information for a read-only data retrieval tool, though it could explicitly state it is 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 two sentences. The first sentence states the purpose and method. The second gives clear usage instructions. No redundant or superfluous words; front-loaded with key 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?

The description adequately explains the output (chances for qualification, final, win) and the input (competition slug, optional trials/seed). With no output schema, it provides enough context for an agent to understand return shape. It also mentions the simulation cap. For a Monte Carlo tool, this is 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% (all three parameters described), so baseline is 3. The description adds value by explaining that trials and seed control simulation and reproducibility, and that competition is identified by slug with an example. This provides context 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 the tool returns each team's chances of qualifying from group stage, reaching final, and winning, using a seeded strength-aware Monte Carlo simulation. It specifies the required input (competition slug) and optional parameters for control/reproducibility, distinguishing it from sibling tools like get_match_probabilities which focus on match-level 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 provides clear usage instructions: pass competition slug, and control trials/seed for reproducibility and simulation control. It gives an example slug ('world-cup-2026') and mentions default and cap values. However, it does not explicitly state when to use this tool vs alternatives like get_qualification_scenarios, missing some exclusion 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?

Description discloses deterministic behavior, defaults (42, 10000), cap (50000), and that it returns probabilities. With no annotations, this provides adequate transparency about the tool's behavior, though it could mention if it's safe/read-only.

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 purpose, no wasted words. Efficient and clear.

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?

Covers essential aspects: input parameters, determinism, output type. Lacks explicit output format or structure (no output schema), but 'advancement probabilities' is understandable for an AI agent.

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 descriptions for each parameter. Description adds defaults and cap for 'trials', and example for 'competition', enhancing understanding beyond 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?

Description clearly states verb 'run the seeded Monte Carlo' and resource 'competition's group stage', returning advancement probabilities. Distinguishes from siblings by specifying group stage simulation, whereas siblings like get_tournament_odds and get_qualification_scenarios likely serve different purposes.

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 'competition' slug, pass 'trials' and 'seed' for control and reproducibility. Gives example slug. Lacks explicit when-not-to-use or alternatives, but is sufficient for selecting the tool.

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