DARIMARKET Research

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

Annotations already declare readOnlyHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is covered. Beyond that, the description adds that results are Academy articles and glossary definitions with links, which tells the agent what kind of payload to expect, though it says nothing about result volume or pagination.

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 triggering condition and then the return contract. The topic list is dense but each example earns its place by widening the match surface for an agent routing user questions.

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 single-optional-parameter, read-only lookup tool with no output schema, the description supplies the trigger, the covered subject matter, and the shape of the return (articles and glossary entries with links). Nothing an agent needs in order to call it correctly is missing.

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 the single 'topic' parameter is fully documented in the schema, including the empty-string category-listing behavior and example values. The description reinforces the topic space through its enumerated examples but adds no new syntax or format meaning. Baseline 3 applies when the 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?

States a concrete verb and resource: it returns DARIMARKET Academy articles and glossary definitions with links, and the title/description pair make clear this is educational content rather than betting data. It does not explicitly contrast itself with the sibling data tools (get_todays_games, get_player_hit_rates), but the domain split is obvious enough to an agent.

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?

Gives an explicit trigger ('when the user asks what a sports stats or odds term means or how it works') followed by a concrete list of covered topics, so the agent can match a user question to this tool without guessing. It stops short of stating when NOT to use it or naming a fallback tool, but none of the siblings overlap functionally, so there is little ambiguity to resolve.

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 and openWorldHint=false, so safety is covered; the description goes further by disclosing what is returned (last-5 and last-10 counts, last-5 values, average) and what the tool is not (no prediction). It does not cover sport-specific quirks or what happens when line is omitted, which is the remaining gap.

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?

Front-loaded with the when-to-use trigger and example, then return shape, then the parameter hint, then the disclaimer — a sensible order with no filler. The inline example query is long but earns its place by disambiguating the stat/line/side combination.

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 carries the burden of describing returns and does so at a useful level (last-5/last-10 counts, values, average). For a 4-parameter read tool it is nearly complete, though it is silent on multi-sport stat-key differences and on behavior when line/side are omitted.

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 each parameter already has a description with examples ('pts', 'points', 'rec_yds', 24.5, over/under). The description only restates that `line` is a number to check, adding no syntax or default behavior beyond the schema, so baseline 3 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?

States a specific verb+resource (how often a player has reached a stat number recently) and lists the supported leagues, giving a concrete example query. It is clearly distinguishable from siblings like search_player (which supplies the player slug) and todays_most_consistent_players (forward-looking consistency rather than per-player historical hit rates).

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?

Opens with an explicit trigger ('Use when the user asks how often...') and an example phrasing, then closes with an exclusion ('Describes past games only. It is not a prediction or a recommendation'), which steers agents away from predictive/consistency requests. The schema's player field also points at search_player for slug resolution.

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, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful return context: start times are in Eastern and results include links to darimarket.us stats pages. It does not say what an empty result means (no games scheduled vs. data unavailable) or whether the response is sorted by time.

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 leads with the triggering user intent, followed by the payoff (matchup + Eastern start time) and the darimarket.us link detail. No filler, though the trailing link clause is slightly tangential to selection.

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 one-optional-parameter, read-only lookup with no output schema, the description covers the trigger, league coverage, returned fields, and timezone. The main residual gap is what defines "today" if the user's timezone differs from Eastern, and what an empty slate returns.

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 baseline would be 3, but the description adds real meaning by expanding the enum in user-facing language ("college football" maps to the CFB value) and by listing all leagues, which signals that omitting the parameter yields the all-leagues view. The default behavior is thus clarified beyond the terse schema note.

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 concrete resource and scope: today's games across six named leagues, returning matchups and start times. It is immediately distinguishable from the siblings (player hit rates, player search, consistency rankings, article lookup), none of which deal with schedules.

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 opens with an explicit trigger ("Use when the user asks what games are on today or tonight...") and covers both the schedule and matchup/start-time intents. It stops short of naming alternatives or stating when not to use it, but the trigger is clear enough to route correctly.

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, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds useful context by naming the return artifact (the slug) and the league coverage, but says nothing about ambiguous names, multiple matches, or empty results — behavior an agent needs to interpret the response. With annotations carrying the safety burden, a 3 fits.

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 purpose and usage trigger are front-loaded in two tight sentences. The final "Sports: NFL (NFL), CFB (College football)..." sentence is mostly redundant with the enum, though the CFB gloss and the placement at the end are the only real weak points.

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 two-parameter lookup with full schema coverage and no output schema, the description covers what the tool does, when to call it, and what it returns (a slug). What's missing is how to handle ambiguous or zero-match queries, which is a meaningful gap for a search tool but not a blocking one.

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 both query (with examples and length bounds) and sport (enum) fully documented, so the baseline is 3. The description adds no matching semantics beyond the schema — it doesn't say whether query is fuzzy, partial, or requires exact names. The trailing league list only partly adds value by glossing CFB as College football.

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?

"Find an athlete DARIMARKET tracks and get the slug the stats tools need" states a specific verb (find), a specific resource (player), and the output artifact (slug) that connects it to downstream tools. An agent can tell this apart from get_player_hit_rates or todays_most_consistent_players without opening any schema.

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?

"Use when the user names an NFL, college football, NBA, WNBA, MLB or NHL player and you need their recent stats, team or next game" gives an explicit trigger condition and the intent behind it. It stops short of naming the alternative tools (e.g. get_player_hit_rates) or stating when not to use it, so it lands at 4 rather than 5.

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 and destructiveHint=false, so the description doesn't need to re-litigate safety. It does add genuinely non-obvious behavior: pre-game/inactive-game coverage, exclusion of injured players, and the return payload (last-10 and last-5 counts plus average). The "past results only, not predictions" line usefully prevents the agent from overselling the output.

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?

It is front-loaded with the trigger condition and then layers scope, exclusions, and return shape in short clauses, so every sentence carries information. The one redundancy is enumerating all six leagues in prose when the enum already lists them, which slightly bloats the opening sentence.

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 one-parameter, read-only lookup with no output schema, the description covers the trigger, the time window, the population excluded (injured players), the metric definition, and the shape of the return values. An agent has everything needed to call it and to describe results correctly to a user.

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?

There is a single optional parameter with 100% schema coverage, including an enum of six leagues and a description that empty means all sports with games today. The tool description adds no syntax or default nuance beyond that, so the schema is doing essentially all the work here.

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 gives a concrete verb-plus-resource framing: identify which players in today's games have been most consistent lately, operationalized as "reached a stat number in 9 or 10 of their last 10 games." That is specific enough to act on, but it never distinguishes this tool from the sibling get_player_hit_rates, which plausibly answers a very similar question.

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 opens with an explicit trigger ("Use when the user asks which players in today's or tonight's ... games have been the most consistent lately") and scopes usage to games that haven't started yet. What's missing is a negative boundary against get_player_hit_rates, so the agent must guess which of the two to call for a hit-rate question.

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