NBA Markets Desk

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 safe, side-effect-free nature is covered structurally. The description adds useful domain scope by tying the tool to Kalshi and Polymarket prices and previews the verdict output, but says nothing about rate limits, accuracy assumptions, or edge cases (e.g. price at 0/100).

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 tight sentences: the trigger/question framing is front-loaded, followed by the inputs and the returned outputs. No filler, no repetition of the schema.

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 an output schema present the description need not explain the return payload, and with 100% schema coverage it need not document parameters. It covers purpose, trigger, and output shape. The remaining gap is cluster-level: no pointer to kelly_size for follow-on position sizing.

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 schema descriptions already enumerate every accepted input format (55, "55%", "55¢", "$0.55", 0.55, American odds). The description only paraphrases this as "in cents and your own probability", which is actually narrower than the schema. Baseline 3 is correct when the schema carries the semantics.

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 function: take a market price plus the user's own probability and return the % EV edge and a BUY/SELL/SKIP read. That is a clear verb+resource with an output preview. It does not distinguish itself from the related sibling kelly_size, which an agent might reasonably confuse it with.

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 for 'is this contract mispriced' and 'what is my edge'" gives an explicit when-to-use trigger in the user's own vocabulary. It offers no exclusions and never names the obvious downstream alternative (kelly_size for sizing once edge is known), so sibling routing is left to inference.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=false and destructiveHint=false, so the bar is low. The description still adds real behavioral context beyond them: the output always contains all three representations, and American odds use the no-comma convention (+441 or -200). It does not restate return format in detail, but an output schema exists so that is not 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?

Three sentences, front-loaded with purpose, then output convention, then trigger examples. No filler, and the critical formatting caveat is parenthetical rather than buried. The quoted example phrases are slightly redundant with the purpose sentence, keeping this just below a 5.

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?

A pure two-parameter, read-only converter with an output schema, full schema description coverage and safety annotations. The description covers what it does, what comes back, the input convention quirk, and when to reach for it — 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%, so the baseline would be 3. The description earns above baseline by explaining the no-comma American convention and illustrating accepted input strings ('+150', '62%', '2.5'), which clarifies the number-or-string typing in the schema. It does not, however, add anything not already implied about the range or enum semantics of `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 names a specific verb (Convert) and the exact resources (implied probability, American odds, decimal odds), and states the round-trip behavior (give one, get all three). An agent can immediately distinguish this from calculate_ev or kelly_size, which compute value or stake rather than reformat an odds figure.

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 gives concrete trigger phrasings ('what is +150 as a probability', 'convert 62% to American odds', 'decimal to implied odds'), which is strong positive routing. It stops short of naming a sibling as the alternative or stating when-not to use it (e.g. if you want expected value, use calculate_ev), so it is clear context rather than full when/when-not guidance.

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

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

Annotations cover the safety profile (readOnly, openWorld, non-destructive), and the description adds substantial behavior beyond them: the exact signal vocabulary (WATCH/ARB), the fields returned (gap in pp, cheaper venue, settlement_match), and a tier gate — free returns only the top 3 gaps while Pro returns the full board. That tiering and settlement-comparison context is exactly what an agent needs to interpret results.

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-loads the use case in the first sentence, then lists the returned quantities compactly, and closes with the free/Pro distinction. Slightly dense with quoted trigger phrases and jargon, but every clause carries information and nothing is redundant.

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 return-value burden and does so: it enumerates the output fields and the free/Pro result-set sizes. For a one-optional-parameter read tool, an agent has everything needed to call it and interpret the response.

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 minGap's accepted formats (number or "3pp"/"3%") and default/WATCH-threshold meaning are already documented in the schema. The description mentions the WATCH/ARB signal and "top 3 gaps" but adds no new syntax or filtering semantics beyond what the schema provides, so the 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 — cross-venue arbitrage gaps on the same sports contract between Kalshi and Polymarket — and names the concrete deliverables (gap in pp, WATCH/ARB signal, cheaper venue, settlement_match). This is clearly separable from siblings like ladder_arb, combo_edge, or scan_mispricings, which cover different products or strategies.

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 explicit user-phrasing triggers ("where is the arbitrage", "Kalshi vs Polymarket price gaps") that map well to intent, plus a free-vs-Pro condition. It does not name an alternative tool or state when not to use it, so the routing guidance stops short of full 5-level specificity.

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 usefully adds that the tool is a pure computation returning a stake amount and a risk rating, which tells the agent there are no side effects. It doesn't discuss edge cases such as invalid probabilities or the meaning of the risk rating, keeping it short of a 5.

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 compact sentences, front-loaded with the core action and its inputs/outputs before the example triggers. Every sentence earns its place, though the trailing quoted-phrase list is slightly listy and could be trimmed.

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?

An output schema exists, yet the description still concisely previews the return ('dollar stake and a risk rating'), and it names all four inputs with the optional bankroll implication clear via the schema. For a stateless calculator this is fully sufficient to call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents each parameter in detail (bankroll optionality, accepted price/probability formats, fraction values). The description restates the same inputs — 'win probability, market price (which sets the payout), your bankroll, a Kelly fraction (full / half / quarter / eighth)' — adding only the note that market price sets the payout. Baseline 3 is appropriate 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 specific verb and resource ('Compute the optimal Kelly position size for a prediction-market contract') and goes on to name the exact inputs and outputs, so it is clearly separable from siblings like calculate_ev and convert_probability. An agent knows immediately what this tool returns (dollar stake + risk rating).

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 concrete triggering phrases — "how much should I stake", "what is my position size", "Kelly sizing for this trade" — which give clear context for when to reach for this tool. It stops short of naming when NOT to use it or pointing to alternative siblings (e.g. calculate_ev for edge, not sizing).

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=true, so safety is covered. The description adds genuinely useful behavioral context beyond that: pricing is 'market prices only, no model' and the Kalshi fee is included in the comparison. It omits pagination/return-shape details, but that is a minor 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 usage trigger, followed by scope and a key caveat in two sentences. The example queries are slightly verbose but they earn their place by anchoring invocation phrasing.

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 four optional params and no output schema, the description does the needed work of explaining what the result represents (the cheapest venue per side, fee included) and what it is not (model output). Coverage is adequate for a read-only board tool; only pagination/response-format specifics are unaddressed.

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% (game, team, limit with clamping note, market enum), so the schema carries parameter meaning. The description reinforces that moneyline/spread/total are the markets but adds no syntax or format detail beyond what the schema already documents.

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

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

States a specific resource and scope: Kalshi NBA moneyline/spread/total compared against a named list of sportsbooks at the same line, with the cheapest venue per side. An agent can distinguish this from player-prop siblings (nba_prop_board) and generic tools (best_available) without opening a 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?

The 'Use for...' examples (Knicks moneyline on Kalshi vs the books, cheapest Celtics spread tonight) give concrete trigger phrasings that map directly to invocation. It stops short of naming explicit alternatives or when-not-to-use cases, so it is strong context but not full routing 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?

Beyond the readOnly/openWorld annotations, the description discloses genuinely useful behavior: sample size on every row, a two-season lookback window, Kalshi prop prices shown alongside, questionable players forking into plays/sits branches, and the explicit 'no projection' caveat. This is rich context an agent could not infer from structured fields.

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 'Use for...' trigger phrases and dense with information; nearly every clause earns its place. It is a long single paragraph rather than cleanly segmented, which keeps it from a 5.

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 return-value burden well: it explains that each absent star yields beneficiary rows with per-row sample sizes and side-by-side Kalshi prices, and notes the plays/sits branch behavior for questionable players. An agent knows what it will get back.

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 all four parameters are already documented in the schema. The description echoes the stat concepts (minutes, points, rebounds, assists, usage) but adds no format or default guidance beyond what the schema provides, so the 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: NBA absences from the official injury report plus the teammates whose stats rose without the absent star. The example queries ('Embiid's out tonight — who benefits?') make the intent unmistakable and distinguish it from prop/game board siblings.

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 'Use for...' framing plus two concrete example questions give clear when-to-use context, and 'Historical splits only, no projection' sets a boundary. It never names a sibling alternative (e.g. nba_prop_board) explicitly, so it falls short of a 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, openWorldHint=true, destructiveHint=false, so safety is covered. The description adds genuinely useful behavior: prices are market-only (no model output) and the Kalshi fee is included in the cheapest-venue computation, which materially affects 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?

Front-loaded with the use-case trigger, then the return shape, in two tight sentences with no filler. The verbatim user-question framing is slightly informal for a spec but earns its place by anchoring intent.

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 it does: cheapest venue per side across named books with fees included. Combined with 100% schema coverage on inputs and read-only annotations, an agent has enough to invoke it correctly; only the exact response row shape is unspecified.

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 every parameter (game, team, limit clamping, player, statType with its four options) is already documented in the schema. The description's mention of points/rebounds/assists/threes only restates the statType enum, adding no syntax or format detail beyond the schema. 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?

Names a specific resource (every Kalshi NBA player-prop strike) and the exact output (the same strike priced beside DraftKings, FanDuel, BetRivers, Fanatics, ESPN, Novig, ProphetX, cheapest venue per side). Clearly distinct from siblings like nfl_prop_board, explain_prop, and best_available.

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 opening quote — "where is LeBron's points line cheapest tonight" — gives a concrete trigger scenario that makes the intended use obvious. However, it states no when-not condition and does not explicitly route the agent to alternatives (e.g. nfl_prop_board for NFL props, explain_prop for explanations).

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