Player-prop lines, market metadata, and line movement over REST/MCP. 14 sports, CoD projections.
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Average 4.5/5 across 13 of 13 tools scored. Lowest: 3.8/5.
Each tool targets a distinct resource and action: find_game resolves team names to eventId, list_games enumerates the slate, scan_props flattens all props, find_player_props filters by player, get_game_props scopes to a game, and get_prop_evidence, get_player_context, get_prop_history each cover a different aspect of a single prop or player. The descriptions explicitly contrast when to use one vs. another (e.g., scan_props vs get_game_props vs find_player_props), so an agent can reliably pick the right tool.
All tool names follow a consistent lowercase snake_case verb_noun pattern. The verbs (find, get, list, scan) accurately reflect the operation, and the nouns are singular or plural appropriately (find_game, list_games, scan_props, get_game_props). No camelCase, no vague verbs like 'process' or 'handle', and no irregular naming conventions.
13 tools is a well-scoped set for a sports props data API. Each tool serves a clear purpose: discovery (list_sports, list_games, find_game, scan_props, find_player_props), per-game or per-prop retrieval (get_game_props, get_prop_evidence, get_prop_history), context (get_player_context, get_market_metadata), and analytical extras (get_leaders, scan_movers). None feel redundant, and the count is within the ideal 3-15 range.
The tool set provides comprehensive read-only coverage of the domain: sport discovery, game discovery, prop browsing by game/player/cross-game, player context, prop-level evidence, line history, market metadata, movers, and leaderboards. Since the API is explicitly read-only and data-focused, there are no missing CRUD operations, and the inclusion of get_strategy_doc is a harmless extra. There are no obvious dead ends or gaps that would cause agent failures.
find_gameResolve team names to an event idAInspectTranslate a matchup (home team + away team) into the eventId needed by get_game_props. Read-only. No side effects. Requires an API key; rate-limited per your tier. Use this when you know the teams playing but don't have the eventId. On success returns: { eventId }. Pass that id straight to get_game_props. On failure returns an error explaining that the game was not found on today's board. If multiple games match the team names (rare), returns the first match sorted by start time. Matching is case-insensitive substring containment against the full team name (e.g. "Yankees" or "New York Yankees" resolve for MLB) — short codes like "NYY" or "NY" do NOT match unless they are literally a substring of the team name. If no game is found, the matchup may not be scheduled for today or the sport may be off-season. When to use: when you know both team names and want the eventId without browsing the full slate. When not to use: use list_games instead when you want to browse all games on the slate. Use get_game_props directly if you already have the eventId.
| Name | Required | Description | Default |
|---|---|---|---|
| away | Yes | Away team name or city, matched the same way. Examples: "Red Sox", "Warriors", "Buffalo Bills", "Arsenal". | |
| home | Yes | Home team name or city, matched as a case-insensitive substring of the full team name. Examples: "Yankees", "Lakers", "Kansas City Chiefs", "Manchester City". Short codes like "LAL" will not match unless literally a substring of the full name. | |
| sport | No | Sport id (mlb, nba, nfl, nhl, soccer, etc.). Omit to default to the current in-season sport. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and excels: it discloses read-only nature, no side effects, API key requirements, rate limiting, matching semantics (case-insensitive substring, no short codes, first match sorted by start time), and failure behavior (game not found, off-season). This is exhaustive and accurate.
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 longer than typical but every sentence adds necessary detail—auth, matching rules, failure behavior, alternatives. It is front-loaded with purpose, well-organized, and free of filler. Slight trim for packaging could improve it, but it remains efficient given the complexity.
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 compensates fully: it describes the success return shape ({ eventId }), error behavior, edge cases (multiple matches), and prerequisites (API key, rate limit). It also covers sport defaulting via the schema. Nothing needed for correct invocation 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% with good descriptions and examples, so the bar is lower. The description adds significant value by explaining substring matching, short-code exclusions, and the 'first match' tie-breaking rule, which enriches understanding beyond the schema's basic parameter 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 opens with 'Translate a matchup (home team + away team) into the eventId needed by get_game_props.' This is a specific verb+resource construction that clearly differentiates from siblings like get_game_props and list_games. The purpose is unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use this when you know the teams playing but don't have the eventId' and 'When not to use: use list_games instead... Use get_game_props directly if you already have the eventId.' This provides clear when/when-not guidance with named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_player_propsFind all active props for a playerAInspectEvery active prop for one player across today's board for a sport — same rows as scan_props, filtered by name (exact normalized match preferred, case-insensitive contains match as a fallback; see matchType in the response) instead of stat. Use this when you know the player but not which game/event they're in.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Player name or gamertag, e.g. "Judge" or "Shotzzy" | |
| sport | No | Sport id. Defaults to the in-season sport. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses matching behavior (exact normalized match preferred, case-insensitive contains fallback) and mentions the matchType field in the response. It also clarifies that it returns the same rows as scan_props, but does not detail edge cases such as no matches 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 primary function and followed by a succinct usage note. No redundant wording; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description provides enough context by referencing scan_props rows and mentioning matchType in the response. It omits details like pagination or handling of unknown players, but for a straightforward query tool the information is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes both parameters (name and sport), achieving 100% coverage. The description adds value beyond the schema by explaining how the name parameter is matched (exact vs. fallback) and that filtering is by name rather than stat, which gives important context for 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 states a specific action: listing every active prop for one player across today's board for a sport. It clearly distinguishes itself from sibling scan_props by noting the difference in filtering (by name vs. by stat) and by providing a direct usage criterion (when you know the player but not the game).
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 explicit guidance: 'Use this when you know the player but not which game/event they're in.' Also contextualizes against scan_props, indicating when this tool is the appropriate alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_game_propsGet player props for a specific gameAInspectFetch all player props for one game identified by eventId. Read-only. No side effects. Requires an API key; rate-limited per your tier. Returns: { eventId, sport, homeTeam, awayTeam, startTime, props: Array<{ player, stat, line, overOdds, underOdds, bookCount, gameState?, flashProjection? }>, sources: string[], fetchedAt, delayed }. flashProjection (present for modeled markets, CoD + MLB today, when available) is { value, sampleN, method, marketKey }, the Flash season-baseline projection, never fabricated. overOdds and underOdds are American-format integers (e.g. -110, +115); null when odds are not available. The stats parameter filters to specific markets (e.g. "points,rebounds" for NBA, "strikeouts,hits_allowed" for MLB). Typical workflow: (1) call list_games to get eventIds, (2) call get_game_props with the eventId. Alternatively, call find_game with team names to resolve the eventId when you know the matchup. Event ids are prefixed ud- (Underdog Fantasy source) or bv- (Bovada source). Returns an error when the event id is not found, the game has ended with no active props, or lines have not been posted yet. When to use: when you have an eventId and want all props for that specific game. When not to use: use scan_props instead when you want a cross-game market view (e.g. all strikeout props today). Use find_player_props when you know the player name but not which game they are in.
| Name | Required | Description | Default |
|---|---|---|---|
| sport | No | Sport id (nba, mlb, nfl, etc.). Must match the sport the event belongs to. Omit to use the current in-season sport. | |
| stats | No | Comma-separated list of stat keys to return, e.g. "points,rebounds,assists" for NBA or "strikeouts,hits_allowed" for MLB. Omit to return all available markets. | |
| eventId | Yes | Event id from list_games or find_game. Prefixed ud- or bv-, e.g. "bv-26839935" or "ud-119284". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explicitly states 'Read-only. No side effects.' and discloses API key requirements, rate limiting, error conditions (event not found, game ended, lines not posted), and a reliability guarantee ('never fabricated') for flashProjection. This goes beyond what annotations would typically cover.
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 front-loaded with a clear, one-sentence purpose, followed by structured details (return shape, workflow, usage boundaries) with no redundant sentences. Although it is long, every section adds necessary context for the tool's complexity, making it highly 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?
With no output schema, the description fully explains the return structure, including nested props, optional fields, American odds formatting, and error behavior. It also covers workflow, parameter semantics, and alternatives, leaving no significant gap for an agent to invoke and interpret the tool 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 description coverage is 100%, with detailed descriptions for eventId, sport, and stats including examples, prefixes, and omit behavior. The tool description adds minimal new parameter information beyond the schema—mainly repeating examples and the return object—so the 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 opens with a specific verb and resource: 'Fetch all player props for one game identified by eventId.' It clearly distinguishes the tool from siblings by naming scan_props and find_player_props in the 'When to use/When not to use' section, making the purpose 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?
Explicit when-to-use and when-not-to-use guidance is provided: 'use scan_props instead when you want a cross-game market view' and 'Use find_player_props when you know the player name but not which game they are in.' It also outlines a typical workflow (list_games → get_game_props) and an alternative (find_game), giving an agent clear decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_leadersRankings: top gaps, recent form, sample strength (Pro)AInspectRanked boards for a sport with a Flash pack. Read-only. No side effects. Pro tier and above (free/starter get a deny message). metric=gap ranks the widest Flash-vs-book edges on the live board (player, market, bookLine, flashLine, gap, gapPct); metric=form ranks players by recent mean vs their Flash Line (delta); metric=sample ranks by baseline size. Returns { sport, metric, stat, count, leaders[], note }. Works for any sport with a pack (CoD and MLB today). No picks -- a ranking of real numbers. When to use: "biggest CoD/MLB edges tonight", "who is trending over their projection". When not to use: for one prop use get_prop_evidence; for raw rows use scan_props.
| Name | Required | Description | Default |
|---|---|---|---|
| stat | No | Restrict the gap board to one market, e.g. "kills_on_game_1". | |
| limit | No | Max rows (default 20). | |
| sport | No | Sport id with a Flash pack (cod, mlb). Defaults to cod. | |
| metric | No | gap (default), form, or sample. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and excels: states 'Read-only. No side effects,' discloses tier-based access behavior ('free/starter get a deny message'), specifies current sport availability ('CoD and MLB today'), and describes the return object shape. It even clarifies 'No picks -- a ranking of real numbers' to prevent misuse.
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 bit long (8 sentences) but every sentence is dense with useful information: purpose, safety, tiers, metrics, return shape, exclusions, and examples. It is front-loaded with the core purpose and then expands logically. Loses one point for slight verbosity, but 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?
For a multi-mode tool with no output schema and no annotations, the description covers purpose, all metrics, return object, access control, examples, and sibling distinctions. It does not explicitly detail the fields in leaders[] for the form and sample metrics (only gap lists fields), and the 'note' field is unexplained, leaving minor 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 covers all four parameters with descriptions, so baseline is 3. The description adds semantic detail for the metric enum (explaining what each value computes) and clarifies sport availability, going beyond the schema. However, it doesn't add much for stat or limit beyond what schema already provides, so not a 5.
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 opens with 'Ranked boards for a sport with a Flash pack,' clearly stating the verb (rank) and resource (sport boards). It distinguishes three metrics (gap, form, sample) and explicitly contrasts with siblings via 'When not to use' (get_prop_evidence, scan_props). This is a specific, differentiated purpose.
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 explicit when-to-use examples ('biggest CoD/MLB edges tonight', 'who is trending over their projection') and when-not-to-use with named alternatives. It also notes the Pro tier requirement, giving clear guidance on access conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_market_metadataMarket metadata (stat vocabulary) for a sportAInspectReturn the machine-readable stat vocabulary for a sport: for each live market, its label, family, scope (map1/maps13/full_game), unit, display order, and whether a Flash projection is supported (with a reason when not). Read-only. No side effects. Rate-limited per your tier. Returns { sport, count, markets: Array<{ statKey, label, family, scopeKind, scope, scopeLabel, unit, displayOrder, uiGroup, projection: { supported, reason }, contextSupported, lineOnly, alternateLine }> }. This is what turns a raw stat key like "kills_on_game_1" into "Map 1 Kills" (kills family, map scope, projectable) so you can label and group props without guessing. When to use: after scan_props / get_game_props, to explain or group the raw stat keys you got back. When not to use: if you only need one sport's existence/access, list_sports already carries marketFamilies.
| Name | Required | Description | Default |
|---|---|---|---|
| sport | No | Sport id (cod, nba, mlb, ...). Omit for the current in-season sport. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does an excellent job: it explicitly states 'Read-only. No side effects.' and 'Rate-limited per your tier', plus clarifies the projection field with a reason. This goes well beyond the bare minimum and helps the agent anticipate behavior.
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 well-structured and front-loaded with the core purpose, followed by an illustrative example and clean usage pointers. It is slightly long, but every sentence adds value, so it never feels wasteful.
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 schema and lack of an output schema, the description is remarkably complete: it documents the exact return shape, provides a concrete example, cites behavioral constraints (read-only, rate limit), and gives usage vs. non-usage guidance. Nothing important is missing for an agent to select and call this tool confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully describes the 'sport' parameter (with 100% coverage), including the optional omit behavior. The description adds no new parameter-level detail beyond what the schema provides, so the 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 opens with a specific verb+resource ('Return the machine-readable stat vocabulary for a sport') and details the exact output structure, making the purpose unmistakable. It also distinguishes from siblings by referencing scan_props/get_game_props and list_sports, so an agent can tell this tool apart.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit 'When to use' and 'When not to use' sections give concrete usage context: use after scan_props/get_game_props to explain or group raw stat keys, and avoid if only existence/access is needed because list_sports already covers it. This is ideal guidance for selecting among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_player_contextPer-mode averages, recent form, and sample size for a playerAInspectSeason context for a player from the sport's Flash pack: per-market baselines, recent form, and sport-native splits (CoD also carries per-mode stats + a map log). Sports with a registered pack (CoD and MLB today) return data; other sports and unmatched players return an honest empty context, never fabricated. Pro tier and above; free/starter keys get a structured tier_required deny.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Player gamertag, e.g. "Shotzzy". Exact match (case-insensitive) — no fuzzy guessing. | |
| sport | No | Sport id (cod, mlb, ...). Sports with a Flash pack (cod, mlb today) return data; others an empty context. Defaults to cod. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the behavioral disclosure burden. It honestly states that unsupported sports or unmatched players return an empty context, never fabricated, and that free/starter keys receive a structured tier_required deny. It also notes CoD-specific extras like per-mode stats and a map log.
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, front-loaded with the core purpose. Every clause adds meaningful information (sport coverage, tier requirement, empty-context behavior), 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 (2 parameters, no output schema, no annotations), the description is sufficiently complete. It covers purpose, supported sports, tier gating, and empty-context behavior. It could mention the return format in more detail, but the essential information for invoking the tool correctly is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% description coverage for both parameters, setting a baseline of 3. The description adds valuable context beyond the schema by clarifying sport availability (which sports return data) and reinforcing the exact-match requirement for the player name, elevating it to a 4.
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 identifies the tool as providing season context for a player, including per-market baselines, recent form, and sport-native splits. It distinguishes itself from sibling player-focused tools like find_player_props and get_prop_evidence by focusing on overall context rather than specific props or evidence.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states that only sports with a registered Flash pack (CoD and MLB today) return data, while others return empty context. It also specifies the Pro tier requirement for access. It does not explicitly reference alternative sibling tools, but the conditions for when to use this tool are clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_prop_evidenceOne prop's full evidence (book line, Flash line, gap, form, splits, movement)AInspectAssemble the whole story of a single prop in one call. Read-only. No side effects. Rate-limited per your tier. Pro tier and above; free/starter keys get a structured tier_required deny. No picks, no advice -- a data feed. Returns { found, sport, player, market, event, book:{line,source,odds}, flash:{line,rawBaseline,method,sampleN,sampleStrength,mode}, gap, gapPct, form:{n,recentMean,median,stdev,min,max,hitRateVsBookLine,hitRateVsFlashLine,recent}, splits, modeSplit, opponentSplit, recentMaps, movement:{openedLine,currentLine,delta,series?}, freshness:{contextGeneratedAt,ageHours,stale,observedThrough,observedAgeHours,observedStale,note}, missingReasons, disclaimer }. freshness splits two clocks: ageHours/stale is the pack BUILD age; observedThrough/observedAgeHours/observedStale is the real age of the form data (can be far staler if ingest froze), with note a human "Form data through " line. hitRateVsBookLine is how often recent maps went over the POSTED line (what a bettor asks); hitRateVsFlashLine is vs the Flash projection. The movement point series is Pro-only (free gets opened/current/delta). Available for sports with a registered Flash pack (CoD and MLB today); other sports return found:false with a reason, never fake data. Every absent section carries an explicit missingReason instead of being silently dropped. When to use: to explain or justify one prop end-to-end (why the Flash line sits where it does, how the player has trended, whether the line moved). When not to use: for a broad market view use scan_props; for a player overview use get_player_context.
| Name | Required | Description | Default |
|---|---|---|---|
| stat | Yes | Stat key, e.g. "kills_on_game_1". Call get_market_metadata for the vocabulary. | |
| event | No | Optional event id (ud-...) to disambiguate multi-game slates. | |
| sport | No | Sport id. Sports with a Flash pack (cod, mlb today) return full evidence; others found:false with a reason. Defaults to cod. | |
| player | Yes | Player gamertag, e.g. "Dashy" (exact normalized match preferred, contains-match fallback). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and nails it: read-only, no side effects, rate-limited, tiered access, Pro-only movement series, no fake data, and explicit missingReasons rather than silent drops. It also explains the two-clock freshness system, which is a subtle behavioral detail crucial for interpretation.
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 long but every sentence earns its place—behavioral caveats, return-field summary, and usage guidance are all necessary for a tool with this complexity. It is front-loaded with the core purpose and structured with clear sections, though a bit dense.
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 compensates by detailing the return shape (found, book, flash, gap, form, splits, movement, freshness, missingReasons) and key behaviors like tier restrictions and sport availability. Given the high complexity, this is remarkably 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 baseline 3 applies. The description adds some context beyond schema (sport availability behavior, defaults for sport, optional event disambiguation) but largely the parameters are well documented in the schema itself. The added value is marginal, not transformative.
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 opens with a specific verb+resource: 'Assemble the whole story of a single prop in one call.' It clearly distinguishes from siblings by naming scan_props for broad market view and get_player_context for player overview, so the agent knows exactly what this tool uniquely provides.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit 'When to use' and 'When not to use' sections provide clear context, including alternatives (scan_props, get_player_context). It also states sports availability (CoD and MLB today) and tier requirements, giving the agent explicit selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_prop_historyLine history for a prop (Pro)AInspectChronological line/odds history for a player prop, with opened/current/movement. Pro tier and above — free/starter keys get a deny message pointing at the upgrade. History accrues from when archiving started, so early results may be short.
| Name | Required | Description | Default |
|---|---|---|---|
| stat | No | Stat key, e.g. total_bases. | |
| event | No | Restrict to one event id. | |
| limit | No | ||
| sport | No | Sport id, e.g. mlb. | |
| player | Yes | Player name (case-insensitive contains match). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the tier restriction and the deny behavior, and explains that history may be short for early results due to archiving start. This is useful non-obvious context beyond the schema.
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 states the core function, second provides essential caveats. No wasted words, information is front-loaded, and every clause 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?
Despite lacking an output schema, the description gives enough context about the tool's purpose and key limitations. It explains what the history contains (opened/current/movement) and the tier/archiving caveats. It doesn't explicitly describe the response shape, but for a simple history listing, the provided detail is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 80% of parameters with descriptions. The description adds no additional parameter-level detail beyond the schema, but it clarifies the output content (opened/current/movement) which indirectly relates to what the parameters filter. 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 returns a chronological line/odds history for a player prop, including opened/current/movement. This distinguishes it from siblings like scan_movers (current odds) and get_prop_evidence, though it does not explicitly name an alternative.
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 a Pro-tier requirement and that free/starter keys receive a deny message, and mentions a data-availability caveat (history starts from archiving). However, it does not explicitly guide when to use this tool versus other sibling tools, such as get_prop_evidence or scan_movers.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_strategy_docFlash Odds Automation OS — architecture & strategy documentAInspectReturns the Flash Odds Automation OS strategy document (2026-07-17). 14 sections covering: current architecture map, automation scorecard (26/70), event model, state machine, social operating strategy, live automation maturity ladder, competitive differentiation thesis, proprietary analytics catalog, product experience, prioritized roadmap, and a concrete build-now engineering spec for the CDL result-provider adapter. Use this to understand how Flash Odds is built, what is and is not automated, what to build next, and the core design invariants (frozen claims, no fabrication, approval-gated X posts, Chamber loopback-only). Read-only. No side effects. No API key required.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explicitly discloses that the tool is read-only, has no side effects, and requires no API key. It also gives a detailed preview of the document's contents, which helps set expectations. However, it doesn't mention what the actual response format is (e.g., raw text, JSON) or any potential length, which would be useful but not critical.
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 longer than necessary but every sentence carries valuable information. The first sentence is front-loaded with the core purpose and version. The list of sections and design invariants adds useful detail without rambling. The final sentence about read-only and API key is concise. Slightly verbose but not wasteful.
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 no-parameter document retrieval tool with no output schema, the description is complete. It covers the resource, its contents, the purpose for using it, safety profile, and authentication. There is nothing essential missing for an agent to decide whether to invoke this 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?
The tool has zero parameters, and the schema is empty with 100% coverage. Baseline for zero parameters is 4. The description correctly avoids inventing any parameters and focuses on the return value. No additional parameter info is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Returns') and names the exact resource ('Flash Odds Automation OS strategy document'), including version date. It clearly distinguishes itself from the sibling tools, which all relate to game/prop data retrieval, by focusing on the architecture and strategy document.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use this to understand how Flash Odds is built, what is and is not automated, what to build next, and the core design invariants.' It also adds usage context with 'Read-only. No API key required.' However, it doesn't mention any alternatives or exclusions, but given the sibling tools are clearly different in purpose, this omission is minor.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_gamesList today's gamesAInspectReturn today's games that have player props available for a sport. Read-only. No side effects. Requires an API key; rate-limited per your tier. Returns: { sport, count, games: Array<{ id, sport, homeTeam, awayTeam, startTime, live, source }> }. id is the eventId to pass to get_game_props (prefixed ud- for Underdog or bv- for Bovada); live is true when the game is in progress; source is "underdog" or "bovada". Live games sort first; scheduled games follow. Typical workflow: call list_games to discover eventIds, then pass an eventId to get_game_props. If sport is omitted the server selects the active in-season league automatically. Returns count=0 with an empty games array (not an error) when no props are posted yet for the day. When to use: to browse all games on the slate or to find an eventId before calling get_game_props. When not to use: if you already have the eventId, skip this and call get_game_props directly. Use find_game instead when you know the team names but want a single-game eventId without browsing the full slate.
| Name | Required | Description | Default |
|---|---|---|---|
| sport | No | Sport id: nba, mlb, nfl, nhl, ncaab, ncaaf, soccer, or cod. Omit to default to the current in-season sport. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses read-only behavior, no side effects, API key requirement, rate limiting, return format, sorting order, and the count=0 edge case. It also explains the id prefixes and the live flag semantics, providing comprehensive behavioral 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?
Although the description is long, every sentence adds value: purpose, safety, return type, field explanations, workflow, edge case, and usage guidance. It is front-loaded with the core statement and structured logically, earning its length.
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 annotations and no output schema, the description is fully self-contained. It explains the return shape inline, clarifies the relationship to get_game_props, and covers the empty-result case. It also differentiates from find_game, making it complete for an agent to decide when and how to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description already documents the sport parameter's values and the default behavior when omitted ('Omit to default to the current in-season sport'). The tool description repeats this same information ('If sport is omitted the server selects the active in-season league automatically') without adding new meaning beyond the schema. Thus the baseline of 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?
The description states a specific verb ('Return') and resource ('today's games that have player props available for a sport'), clearly distinguishing it from sibling tools like find_game and get_game_props. It explicitly notes the scope and the return type, leaving no ambiguity about the tool's purpose.
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 explicit when-to-use ('to browse all games on the slate or to find an eventId before calling get_game_props'), when-not-to-use ('if you already have the eventId, skip this'), and names an alternative tool (find_game) for a different use case. This is the clearest possible guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_sportsList sports with live status + capabilityAInspectList every sport supported by Flash Props API with its live status and how deep the Flash model goes. Read-only. No side effects. Rate-limited per your tier. Returns { sports: Array<{ id, name, category, enabled, status, activeGames, activeProps, projectedProps, projectionCapability, effectiveProjection, contextCapability, marketFamilies, supportedMarkets, sources, lastFetchedAt, cacheAgeSeconds, shapeCanaryTripped, legalLine, notes }> }. id is what you pass as the sport parameter to other tools. status: "live" = props posted now, "idle" = in-season but none posted, "offseason" = out of season. projectionCapability tells you WHERE the depth is: "advanced_context" (CoD: projections + form + splits + map logs) and "flash_projection" (MLB: real season baselines + form + splits) vs "posted_lines_only" (everything else today). contextCapability "deep" means get_player_context returns data; "none" means it will be empty. enabled=false means the sport is outside your tier. When to use: to discover valid sport ids, or to check which sport actually has projections/context before asking for them. When not to use: if you already know the sport id and just want its props.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description takes full responsibility and discloses read-only nature, no side effects, rate limiting, and explains the meaning of status field and capability flags (e.g., projectionCapability, contextCapability). This goes far beyond structural metadata.
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 long but well-structured; it front-loads the core purpose before diving into field semantics and usage guidance. Each sentence adds value, though a few examples are somewhat repetitive. It remains appropriately sized given the complexity of the return object.
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 zero-parameter read-only tool with no output schema, this description provides a comprehensive picture of what the tool returns and how to interpret it, including tier restriction (enabled), field meanings, and when to use alternatives. Nothing important 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?
The tool has zero input parameters and an empty schema, so the baseline is 4. The description exceeds this by detailing the output structure and explaining the semantics of key fields like id, status, and capabilities, which is essential for interpreting results correctly.
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 opens with a specific verb 'List every sport supported by Flash Props API' and explicitly states what is returned (live status and capability depth). This clearly distinguishes it from siblings like list_games or get_market_metadata.
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 includes explicit 'When to use' and 'When not to use' guidance, e.g., 'to discover valid sport ids' vs 'if you already know the sport id and just want its props.' This directly addresses tool selection against alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_moversBiggest line movers (Pro)AInspectProps whose line moved most within a lookback window (default 24h, max 7d), sorted by absolute movement. Pro tier and above — free/starter keys get a deny message pointing at the upgrade.
| Name | Required | Description | Default |
|---|---|---|---|
| stat | No | Stat key, e.g. passing_yards. | |
| limit | No | ||
| since | No | Lookback window: e.g. 6h, 24h, 3d (1h min, 7d max). Defaults to 24h. | |
| sport | No | Sport id, e.g. nfl. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the sorting behavior, time window defaults and limits, and the tier-based access control with a deny message, which is valuable context beyond the schema. It does not detail the response structure or error handling beyond the deny message, but the key behavioral traits are 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?
The description is two sentences, front-loaded with the core purpose and then the access constraint. Every word is meaningful, with no redundancy or filler.
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 scan tool with no required parameters and no output schema, the description covers the key aspects: what it returns, ordering, time window, and access. It does not explicitly state sort direction (though 'biggest' implies descending) or whether sport/stat are optional filters across all sports, which are minor 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?
The schema already describes 3 of 4 parameters (stat, since, sport) with examples and constraints, giving 75% coverage. The description repeats the since default and max but adds no new parameter semantics beyond what the schema provides, so a neutral score 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 it returns 'Props whose line moved most within a lookback window' with a specific ordering ('sorted by absolute movement'). This distinguishes it from sibling tools like scan_props by focusing on line movers, and the title reinforces the purpose.
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 provides clear context on the lookback window (default 24h, max 7d) and the sorting behavior. It also notes the Pro tier restriction with a deny message for lower tiers, but does not explicitly compare against alternative tools like scan_props or get_prop_history.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_propsScan all props across today's slate (market-wide feed)AInspectFlatten every active player prop across all of today's games for a sport into a single list. Read-only. No side effects. Requires an API key; rate-limited and row-capped per your tier (free: 25 rows, starter: 100, pro+: 500). Returns: { sport, stat, count, rows: Array<{ player, stat, line, overOdds, underOdds, bookCount, gameState?, flashProjection?, eventId, sport, homeTeam, awayTeam, startTime, source, fetchedAt }> }. Each row is a player prop merged with its event context — there is no team or opponent field; use homeTeam/awayTeam instead. overOdds/underOdds are American-format integers; null when odds unavailable. Use scan_props when you need a broad cross-game market view — e.g. every player with a strikeouts prop today, all passing-yards lines for the slate, or which players have the most active markets. Returns count=0 with an empty rows array (not an error) when no props are posted for the day yet. When to use: broad market discovery across all games for a sport. When not to use: use get_game_props instead when you already have an eventId and want one game only — it is faster and not row-capped. Use find_player_props instead when you want all markets for one player by name.
| Name | Required | Description | Default |
|---|---|---|---|
| stat | No | Filter to exactly one stat market, e.g. "strikeouts", "points", "passing_yards". Omit to return all stat types. | |
| limit | No | Maximum number of rows to return. Capped at your tier limit (free=25, starter=100, pro+=500). Omit to return up to your tier maximum. | |
| sport | No | Sport id (nba, mlb, nfl, etc.). Omit to use the current in-season sport. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden. It thoroughly covers behavioral aspects: 'Read-only. No side effects,' API key requirement, rate limiting and row caps per tier, and the exact return shape. It also discloses edge cases like empty rows returning count=0, null odds handling, and the absence of a team/opponent field. This is richly 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?
Though longer than typical, every sentence in this description earns its place. It is well-structured with a clear purpose statement, return format details, usage examples, and exclusion guidance. The information is front-loaded and logically organized, avoiding redundancy while providing comprehensive details.
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 no annotations, so the description must be self-sufficient. It fully specifies the return structure, example use cases, rate limits, parameter behavior, and exceptions. The completeness is exceptional—an agent can confidently select and invoke this tool based solely on the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides complete descriptions for all three parameters (stat, limit, sport) with 100% coverage. The description adds context around tier capping for limit and examples, but these are also present in the schema. Since the schema does the heavy lifting, the description adds marginal value beyond it, justifying the baseline score of 3.
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 opening sentence clearly states the tool's function: 'Flatten every active player prop across all of today's games for a sport into a single list.' It uses a specific verb ('flatten') and uniquely identifies the resource (player props across all games). The description further distinguishes itself from siblings by explicitly naming get_game_props and find_player_props as alternatives, ensuring no ambiguity about its scope.
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 explicit usage guidance with a dedicated 'When to use' and 'When not to use' section. It states to use scan_props for broad cross-game market discovery and explicitly names alternatives (get_game_props for a single game, find_player_props for one player) along with rationale ('faster and not row-capped'). This is exactly the kind of clear context an agent needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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