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

With no annotations, the description carries the full burden. It usefully discloses a default-injection behavior (IV estimated at 0.28 India F&O typical when omitted) and that vwap toggles a hold-above/exit-below filter, but it never says the tool is a pure computation with no side effects, nor how errors or missing inputs are handled.

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 leads and the Args list follows; every line carries information that is unavailable in the schema. No filler sentences or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 9-parameter computational tool with no output schema and no annotations, the description covers inputs well and summarizes the outputs at a high level. Edge cases (expiry-day handling, units for entry/spot) are left implicit, which keeps it just short of 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 0%, so the Args block is doing all the work — and it does it: every one of the 9 parameters is documented with meaning, including 'C'/'P' for right, the lot-size multiplication example for qty_shares, and the vwap/iv conditional behavior. This is a genuine semantic upgrade over bare schema titles.

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 ('Score an open long option position') and enumerates the outputs (breakeven, current P&L, hold/exit framing). The 'open long position' scope implicitly separates it from market-wide siblings like option_chain, max_pain, and greeks, so an agent can route correctly.

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?

Usage is implied by the scope (an already-held long option) and there is per-argument guidance ('vwap ... if known', 'pass a real one when known'), but the description never states when to choose this over siblings such as greeks or strategy_payoff, nor any exclusions for short/complex positions.

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

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

No annotations are provided, so the description carries the full behavioral burden. It discloses useful behavior by naming the Black-Scholes model and explaining that market_price causes IV to be re-solved instead of using the iv argument, but it omits assumptions such as risk-free rate/dividends and does not describe what greeks are returned.

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 the tool's purpose and then uses a compact Args list for parameter semantics. Every line contributes directly to invocation or interpretation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is sufficient to select and call the tool with correct parameter meanings, but it lacks return-value details and model assumptions. With no output schema and no annotations, more context about the returned greeks and calculation assumptions would improve completeness.

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

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

Schema description coverage is 0%, so the description must compensate, and it does: it documents spot, strike, calendar days to expiry, annualized IV as a decimal, call/put right, and the market_price override behavior. This adds substantial meaning beyond the schema's bare titles.

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 calculation and resource: "Black-Scholes greeks for one option." It is clear what the tool computes, though it does not explicitly differentiate itself from sibling tools such as option_chain or analyze_position.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It explains parameters but does not state usage contexts, exclusions, or when a sibling tool would be more appropriate.

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

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

With no annotations, the description carries the full burden, and it does disclose real behavioral traits: the snapshot is fetched from public NSE in-session and may be rate-limited, with a retry recommendation. However, it omits read-only status, permission/auth needs, latency, and failure modes beyond rate limiting.

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 with the payload front-loaded; the second sentence adds operational value rather than filler. Nothing is wasted, though the parenthetical could be slightly more compact.

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?

There are no annotations and no output schema, so the description must compensate more than it does. It names the three output metrics and flags rate limiting, but never explains the response shape, units, or behavior when the chain is unavailable.

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 0% for the single required 'symbol' parameter, so the description is the only source of meaning. 'For an underlying's' implies symbol is the underlying instrument, but it never states the required format (e.g., NSE ticker conventions), leaving a real gap.

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 specific outputs (max pain, PCR, CE/PE walls) for a specific resource (an underlying's live option chain), so the agent knows what it computes. It does not distinguish itself from the sibling option_chain, which likely overlaps heavily.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no sibling is named as an alternative. The agent must infer that this is the tool for max-pain/PCR analytics versus the broader option_chain.

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

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

With no annotations, the description carries the full behavioral burden. It discloses that data is live NSE data and includes LTP/OI/IV around ATM, but it does not mention authentication, rate limits, whether data is delayed, error behavior for invalid expiries, or the response shape.

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 short and front-loaded: the core behavior appears first, followed by compact parameter notes. Every sentence adds useful information without repetition.

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 3-parameter tool with no annotations and no output schema, the description gives enough to invoke it and identifies the returned metrics (LTP/OI/IV). It still lacks a fuller picture of the response structure and operational constraints such as rate limits or error handling.

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 0%, so the description must compensate, and it does: symbol examples are provided, expiry is described as a filter with format and default behavior, and atm_band is explained as the number of strikes either side of the money. Minor gaps remain, such as not restating the atm_band default of 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 states a specific resource and scope: a live NSE option chain around ATM for one underlying, including LTP/OI/IV. It is clear what the tool returns, but it does not explicitly differentiate itself from siblings such as max_pain or greeks.

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?

Usage is implied by the data type: use this when you need an option chain around ATM. However, there is no explicit when-to-use guidance, no exclusions, and no comparison to sibling tools like max_pain or greeks.

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

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

With no annotations, the description carries the full burden; it does disclose meaningful behavior — the analysis is expiry-only, and iv drives a probability estimate rather than a valuation. However it says nothing about output shape (payoff curve vs. scalar), whether it is a pure computation with no side effects, or any rate/limit characteristics.

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 scope sentence is front-loaded, and the Args list is compact and scannable, with the critical sign convention given inline in parentheses. Slightly information-dense but no filler sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 5-parameter analytical tool with no annotations and no output schema, the input side is covered well, but the description never says what the tool returns (payoff at expiry per spot? breakeven? probability?) or in what units, leaving the agent unable to anticipate the result.

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 0%, so the embedded Args block does all the work: it defines each leg field (strike, right 'C'/'P', qty signed + long / - short, premium, optional per-leg lot_size) plus units for spot, calendar days for t_days, and decimal annualized IV. This resolves ambiguities the bare schema (array of additionalProperties objects) leaves completely open.

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 first sentence gives a specific verb+resource+scope: payoff analysis for a multi-leg option strategy, explicitly at expiry. It is distinguishable from max_pain and greeks by the 'payoff at expiry' framing, though it never explicitly contrasts itself with analyze_position, which sounds like the nearest sibling.

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?

There is no when-to-use guidance, no prerequisites (e.g. whether live market data is needed), and no mention of when to prefer analyze_position, greeks, or max_pain instead. The 'at expiry' scope is stated but the agent must infer when this tool is the right choice.

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