TradingCalc MCP — Crypto Futures Math

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

The description discloses that it returns averagePrice, totalSize, and totalCost, but does not explicitly state it is side-effect-free or read-only. More importantly, the top-level 'weighted average' wording contradicts the parameter description's arithmetic/harmonic mean behavior, so the actual calculation is not 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?

The description is concise, with two sentences covering purpose, use case, and return fields. It avoids unnecessary detail and is well-structured for quick comprehension.

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?

It provides enough context for a simple average-entry calculation and names the output fields, but lacks output schema, edge-case behavior (e.g., empty fills, sell fills), and a clear statement of the formula. The weighted-vs-arithmetic contradiction further reduces 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?

Most parameters are described, including the linear/inverse distinction and quantity units for inverse. However, the fills array itself lacks a description, and the relationship between 'weighted average' and the arithmetic/harmonic formulas is not reconciled, leaving some ambiguity about how price and quantity interact.

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 calculates an average entry price from multiple DCA fills and specifies when to use it. However, the phrase 'weighted average' conflicts with the contractType parameter describing linear as an arithmetic mean, creating mild ambiguity about the exact calculation.

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

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

It explicitly says to use when a user has filled at multiple prices and asks for average entry, which is a clear trigger condition. It does not mention alternatives or when not to use it, but the primitive nature and sibling workflows provide enough context.

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

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

No annotations provided, so description carries full burden. It discloses what the tool computes and returns (hedgeNotional, requiredMargin, estimatedFundingCost) and lists input parameters. However, it lacks details on behavioral traits like idempotency, computational constraints, or error handling, which are expected for a calculation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: two sentences front-loading the purpose and usage examples. Every word earns its place; no redundancy or irrelevant 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?

Given 4 parameters (1 required, no output schema), the description explains what the tool does, what it returns, and provides example queries. It adequately covers the core functionality but could mention potential limitations or assumptions for 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 100%, so the input schema already documents all parameters. The description adds minimal extra meaning beyond example usage (e.g., 'Default 1' for leverage is already in schema). Therefore, 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?

Clearly states the tool calculates the short perpetual futures position size for hedging a spot holding, with specific verb 'Calculate' and resource 'hedge ratio'. Examples like 'how much should I short to hedge my BTC?' directly convey the purpose, distinguishing it from sibling tools.

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 use cases via example queries (e.g., 'how much should I short to hedge my BTC?'). While it doesn't explicitly say when not to use or list alternatives, the examples give clear context for when this tool is 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 provided, the description fully carries the behavioral details: it runs tests and returns a pass/fail report with counts and timestamp. It does not introduce any contradictory side effects, and the verification context implies read-only, non-destructive 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 two sentences with no wasted words. It front-loads the action and includes all necessary details: coverage, output, and recommended usage timing.

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 absence of parameters and output schema, the description fully covers what the tool does, what it returns, and when to invoke it. No additional context is needed for an agent to correctly 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 tool has zero parameters, so there is nothing to explain. The description correctly reflects that no input is needed beyond the implicit system state.

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 runs the full regression suite with specific coverage details (35 canonical test vectors across 12 calculators), using the specific verb 'Run' and resource 'regression suite'. It is distinct from sibling tools that perform calculations or workflows.

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 instructs when to use the tool: 'Call this before using results in production workflows', providing a clear precondition for its invocation. It also implies it is for verification rather than calculation, distinguishing it from the sibling calculators.

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

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

With no annotations provided, the description carries the full burden. It transparently describes the action as a calculation and notes the return fields (breakevenPrice, totalFees), implying a non-mutating planning operation. It stops short of explicitly stating 'does not execute trades' or 'no side effects', but the wording is sufficiently indicative.

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 concise sentences, front-loaded with the core purpose and followed by a clear use-case trigger. No fluff or redundant information.

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

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

The description includes expected return fields (breakevenPrice, totalFees), covers default values in the schema, and clarifies inverse-contract fee currency in the schema description. Without an output schema, the return field names provide sufficient context for the calculation tool.

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

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

Schema description coverage is high (83% with 5 of 6 parameters having descriptions, plus enums on two parameters). The description itself adds no additional parameter semantics beyond what the schema already provides, so baseline scoring is appropriate.

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

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

The description clearly states the tool calculates the break-even exit price covering all trading fees, with a specific verb ('Calculate') and resource ('break-even exit price'). It also includes an explicit user query trigger ('what price do I need to just break even?'), distinguishing it from general pricing tools.

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 gives a clear 'Use when' condition tied to a concrete user query. However, it does not explicitly differentiate from sibling tools like workflow.run_funding_breakeven or workflow.run_exit_target, so guidance on alternatives is implicit rather than explicit.

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

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

No annotations provided, so description carries full burden. Mentions optional buy/sell delta but does not disclose if tool is read-only, modifies state, requires permissions, or what the output represents. Minimal behavioral context beyond the core question.

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?

Description is succinct with key concept front-loaded. Two concise sentences with no waste. Slightly more detail on output would improve, but overall 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?

No output schema and no annotations; description does not explain return values (e.g., boolean, score, delta details). For an analytical tool, this leaves the agent guessing about what the tool provides, making it incomplete.

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 each parameter already has a description. The tool description adds the high-level concept of breakout acceptance but does not add further meaning to parameters beyond what the schema provides. 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?

Description clearly states the tool evaluates breakout acceptance in Market Profile context with specific verb+resource ('did price accept/hold beyond value area'). It is distinct from sibling tools like run_session_structure, but does not explicitly differentiate.

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 a concrete use case ('Use for did the break above VAH get accepted?') but does not specify when not to use or list alternative tools. Usage context is implied rather than explicit.

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

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

No annotations are provided, so the description carries full burden. It lists return fields (netYieldPct, grossProfit, etc.) which hints at the analysis nature. However, it does not disclose whether the tool performs any side effects, requires permissions, or has assumptions (e.g., no slippage). The description is adequate but not rich in 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?

The description is three sentences: purpose, usage trigger, and return fields. It is concise, well-structured, and front-loaded with the most important information. Every sentence adds value without 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?

Given no output schema, the description lists the return fields, which is helpful. It covers the core purpose, usage context, and returns. However, it could mention potential error conditions or limitations (e.g., not accounting for exchange-specific fees beyond transfer fee). Overall, it is complete enough for an analytical tool.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds context by linking parameters to the scenario (e.g., long exchange funding rate), but it doesn't provide details beyond what the schema already describes. No significant added meaning.

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 performs 'Delta-neutral carry trade (funding arbitrage) analysis' and specifies the exact use case: 'Use when user asks 'is this carry trade worth it?' — long on exchange A, short on exchange B, collect the funding rate spread.' This provides a specific verb+resource and distinguishes it from sibling tools like workflow.run_funding_arbitrage and workflow.run_funding_breakeven.

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 gives explicit context for when to use the tool ('Use when user asks...') and describes the scenario (long/short on different exchanges). However, it does not explicitly state when not to use it or mention alternative tools among the many siblings. The differentiation from similar tools like workflow.run_funding_arbitrage is implied but not explicit.

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 exist, so the description must bear the full burden. It does not disclose that this is a simulation assuming constant funding rates, nor does it mention limitations or assumptions. It lists output fields but lacks depth on 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?

Three sentences: purpose, usage examples, output fields. No wasted words, information is front-loaded.

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

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

Without an output schema, listing return fields is helpful. The examples clarify typical inputs. However, missing details like growthTable structure or assumption of constant funding rate slightly reduce 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 100%, so the baseline is 3. The description adds example usage that implies parameter roles, but does not provide significant additional meaning beyond the schema.

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

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

The description uses a specific verb 'project' and identifies the resource 'capital growth from reinvesting perpetual futures funding income'. It provides example user queries that distinguish this tool from sibling tools like workflow.run_carry_trade and workflow.run_funding_arbitrage.

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 the tool with two example user queries. It does not mention when not to use or suggest alternatives, but the examples make the usage clear.

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

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

No annotations exist, so description must disclose behavioral traits. It states returns (averageEntry, breakeven, per-level summary) but does not mention side effects, permissions, rate limits, or error handling. Insufficient for a tool with no annotation safety net.

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 plus a returns line. Front-loaded with purpose, then usage, then output. No wasted words. 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?

Adequate for a calculator with 4 parameters and no output schema. Returns fields are mentioned, but no details on per-level summary structure, fee application, or edge cases like invalid entries. Could provide more context.

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 50% (fee params described), but description adds little: only rephrases entries as 'multiple fill prices and sizes' and side as long/short. No extra detail on fee parameters or how they are used. Missing parameter descriptions are not compensated.

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

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

Description clearly states it calculates weighted average entry price, breakeven, and per-level contribution from multiple fill prices and sizes. It specifies it's for DCA entry planning but does not explicitly contrast with sibling primitive.average_entry, so not fully distinguished.

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 use cases: 'when user bought at several prices and asks what's my average entry? or where is my DCA breakeven?' However, it does not mention when not to use it or alternatives like primitive.average_entry for simpler queries.

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 burden of explaining behavior. It states that the tool returns targetExitPrice, but it does not mention formula assumptions, edge cases, or whether fees and leverage are incorporated in the calculation beyond what the schema hints at.

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 contains no filler or redundant information. It is concise while including useful examples.

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 is no output schema, so the description only says the return value is targetExitPrice. It does not explicitly describe output units or how contractType affects the result, though the parameter schema partially covers this. For a calculation tool with several inputs, a bit more context 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 coverage is high and most parameters have descriptions. The description adds context by framing targetMode and targetValue around PnL and ROE, which helps clarify the intended meaning of those fields beyond the raw schema.

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

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

The description clearly states the tool calculates the exact exit price needed to hit a target PnL or ROE percentage, which is a specific and actionable purpose. The usage examples make the intent easy to understand and naturally distinguish it from related planning tools.

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 gives concrete usage examples for both PnL and ROE targets, which helps an agent decide when to invoke it. It does not explicitly mention when not to use it, but the examples are strong enough to imply the intended scope.

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

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

No annotations provided, so description carries full burden. It discloses return fields (netProfitUsdt, annualizedYieldPct, breakevenDays) but does not state whether the tool is read-only, idempotent, or has side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: first defines purpose, second gives use cases, third lists returns. No wasted words, front-loaded with key information.

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

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

Adequately explains the tool's function and returns. Could mention assumptions like constant funding rates or no slippage, but for a calculation tool the current level is sufficient.

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 descriptions cover all 6 parameters (100% coverage), so description does not need to add param details. It does not, which is acceptable; baseline 3 is appropriate.

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

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

Description clearly states the tool calculates funding rate arbitrage profit including annualized yield, net profit, and breakeven days. It distinguishes from siblings like workflow.run_funding_breakeven by focusing on arbitrage across two exchanges.

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

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

Explicitly says use when user asks 'is this funding arb worth it?' or 'how many days to break even?'. Provides clear context but does not mention when not to use or suggest alternatives.

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

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

No annotations are provided, so the description carries full burden. It discloses the return fields (breakevenWithFunding, etc.), but does not mention behavioral traits such as idempotency, no side effects, or prerequisites like wallet connection. It is adequate but could be more explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise, using three sentences to cover purpose, usage examples, and return values. Every sentence adds value with no waste.

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 is a simple calculation with no output schema, the description covers the return fields adequately. It could mention the output is for a single period but overall is complete for its complexity.

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 86% (high), so baseline is 3. The description mentions 'funding cost + fees' but does not add meaning beyond what the schema already provides for each parameter. No additional parameter details are given.

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

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

The description clearly states the tool computes the price move needed to cover funding costs and fees over a holding period, matching the name and providing concrete user queries. However, it does not explicitly differentiate from sibling tools like workflow.run_funding_cost.

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 specific user questions ('how much does BTC need to move for me to profit after funding?') that trigger this tool, giving clear context. It lacks guidance on when not to use it or alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the return values and the sign convention (negative = you pay, positive = you receive), which is valuable. However, it does not disclose assumptions such as a constant funding rate, no compounding, or the frequency of payments (3x per day) beyond what the schema already states. It also leaves 'perIntervalUsdt' undefined regarding which interval it refers to. These gaps mean the behavior is only partially 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?

The description is two sentences: the first states the purpose, the second gives usage triggers and return value semantics. It is concise, front-loaded, and every sentence earns its place. There is no fluff or repetition of schema 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?

For a calculation tool with 6 parameters and no output schema, the description provides the essential return values and sign convention, which is a significant part of what an agent needs. However, it lacks details on the calculation formula, assumptions (e.g., constant rate), and the meaning of 'perIntervalUsdt' (interval length). These are minor for a calculator but would make it more complete. Given the absence of an output schema, the description does a good job but has room to clarify the interval and assumptions.

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 descriptions for most parameters (coverage 83%), and the description adds no extra meaning about the parameters themselves. It only mentions the return values, not the inputs. Since schema coverage is high, the baseline is 3, and the description does not go beyond that baseline.

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 calculates total funding cost (or income) for holding a perpetual futures position, with a specific verb and resource. It also provides example user queries ('how much funding will I pay holding X days?') that make the purpose unmistakable. While it doesn't explicitly differentiate from siblings, the specificity of the scope (holding cost over days) is enough to distinguish it from tools like funding_arbitrage or funding_breakeven.

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 gives explicit when-to-use triggers by quoting user intents ('how much funding will I pay...' or 'is funding eating my profit?'). However, it does not mention any when-not-to-use conditions or point to alternative sibling tools, so it falls short of the full 5 which requires exclusions or explicit alternatives.

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 behavioral burden. It communicates that this is a pure calculation by using 'Calculate' and describing returned values, and it scopes the calculation to isolated-margin positions. It does not disclose assumptions, limitations, or confirm there are no side effects, but for a calculator tool the core behavior is clear.

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 with no filler: the first states purpose and when to use, the second summarizes return values. Every sentence earns its place and the key information is front-loaded.

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

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

The description covers invocation triggers, the output fields, and the isolated-margin scope, while the schema handles parameter details. It could note explicitly that this is a read-only calculation that does not consider actual account balance or cross-margin positions, but nothing essential is missing for calling 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 80%, so the schema already explains most parameters. The description adds only the output field 'distancePct' and does not enrich parameter meaning beyond what the schema provides, meeting the baseline for high 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 uses a specific verb and resource ('Calculate the liquidation price for an isolated-margin futures position') and reinforces the purpose with user-intent triggers ('where will I get liquidated?'). This clearly separates it from sibling tools such as run_max_leverage and run_position_sizing, none of which target liquidation price.

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

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

It explicitly states when to use the tool with quoted example queries, giving clear context for invocation. However, it does not mention when not to use it or name alternative tools for related margin/leverage calculations, so it stops short of a full 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?

With no annotations provided, the description carries the full burden. It states the tool calculates a value and returns specific fields (maxLeverage, marginAtRisk), implying a read-only computation. It does not mention safety, permissions, or side effects, but the nature of a 'calculate' tool is transparent enough.

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 plus a 'Returns:' line, front-loaded with the main action. It is concise and contains no unnecessary information.

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

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

For a simple calculation tool with no output schema, the description adequately covers purpose, inputs, and return fields. It could mention if it relies on real-time data or assumptions, but the provided inputs suggest a self-contained calculation. Overall, it is nearly 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 the description does not need to elaborate on parameters. It mentions parameters in the overall purpose but adds no detail beyond the schema's descriptions. Baseline 3 is appropriate.

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

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

The description clearly states the tool calculates maximum safe leverage using account size, drawdown, and volatility. It provides concrete example queries ('what's the max leverage I should use on BTC?') that differentiate it from sibling tools like liquidation safety or risk-reward analysis.

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

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

The description explicitly says 'Use when user asks...' and gives two examples, providing clear context for when to invoke this tool. However, it does not specify when not to use it or mention alternative tools from the sibling list.

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

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

No annotations provided, so description carries full burden. It describes outputs (open type, levels, scenario) but does not disclose side effects, data freshness, or authorization requirements. Adequate for a read-only analysis tool but leaves behavioral assumptions unstated.

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, front-loaded sentences. Every word adds value: purpose, outputs, example queries. No redundancy or fluff.

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, description adequately explains return values (open location, type, key levels, scenario) but lacks precise structure or data types. Sufficient for a workflow tool with extensive sibling list; minor gaps in depth.

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 7 parameters with descriptions (100% coverage). The description adds no new parameter-level meaning beyond the schema; e.g., 'value_area_rule' details are already in schema. Credit for schema coverage keeps baseline at 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?

Description clearly states tool performs 'Market Profile open analysis' and lists specific outputs (open location, open type, key levels, scenario framing). Provides concrete use examples ('how did BTC open today?'), establishing a distinct purpose from sibling tools like run_session_structure.

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 cases ('Use for...') and provides example queries. Does not explicitly state when not to use or list alternatives, but context implies differentiation from siblings. Lacks explicit exclusion criteria, so not 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?

No annotations are provided, so the description carries the burden. It discloses the return fields (grossPnl, fees, netPnl, netPnlUsdt, roe) which helps, but it does not mention whether the tool has side effects, requires authentication, or any edge-case behavior. A pure calculation tool likely has none, but this is not explicitly stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single purpose sentence plus a trigger and return list. It is tightly written, front-loads the core function, and contains no redundant or vague phrases.

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 return list compensates for the missing output schema, and the contractType parameter explains the difference between linear and inverse margins. However, error conditions (e.g., division by zero, invalid contract type) are not mentioned, which is a minor gap for a calculation tool.

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

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

Schema coverage is 100% with detailed descriptions. Parameters include units (fraction for fees, base coin vs USDT for contractType), examples (0.0002 = 0.02%), and distinctions (linear vs inverse). The descriptions add significant meaning beyond field names.

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 calculates net PnL, ROE, fees, and gross profit/loss for a futures trade. It uses specific verbs and resource scope, distinguishing it from sibling workflows like funding cost or liquidation safety.

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 a concrete trigger ('when user asks what's my profit/loss on this trade?') and specifies the input context. It does not explicitly mention alternative tools, but the unique function makes the choice unambiguous for the given user query.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does state the return values (positionSize, positionUsdt, marginRequired) but does not explicitly note that this is a pure calculation with no side effects (e.g., no order placement, no state mutation). It also does not mention any edge cases or prerequisites (e.g., stopLoss must be below entry for long). This is a moderate level of transparency.

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 concise sentences. The first sentence front-loads the core purpose and inputs; the second provides usage triggers and return fields. There is no fluff or repetition, and every word 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?

For a calculation tool with 8 parameters (4 required) and no output schema, the description is quite complete. It covers the purpose, usage triggers, and return fields. The schema handles parameter details, including the inverse contract nuance. The only minor gap is the absence of explicit edge-case guidance, but that is not critical for an agent to invoke 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 88%, so parameters are already well-documented in the schema. The description adds little beyond echoing riskUsdt and stopLoss as core inputs and mentioning the output fields. It does not clarify the interaction of leverage or fees, but the schema covers their defaults and types. Thus, the description adds minimal extra meaning beyond the schema.

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

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

The description states a specific verb ('Calculate'), a clear resource ('correct position size'), and the key inputs ('maximum risk in USDT' and 'stop-loss price'). It also names the return fields, making the tool's function unambiguous. While it doesn't explicitly name a sibling, the purpose is distinct from other workflow tools like run_risk_reward or run_max_leverage.

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 gives explicit user-phrase triggers ('how many coins should I buy?' or 'size my position so I risk exactly $X'), which is strong usage context. However, it does not mention when not to use it or explicitly contrast it with alternative tools, so it stops short of full exclusion guidance.

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

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

Without annotations, the description carries the full burden and clearly states the tool orchestrates calculations, fetches live funding rate if omitted, and returns specific fields (positionSize, breakeven, etc.). No side effects are mentioned, but it is likely read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences plus a return list, front-loaded with the key purpose. 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?

The description covers the tool's purpose, usage triggers, and return values. Given the complexity (13 parameters, composite tool), it is adequate but could mention error conditions or prerequisites more explicitly.

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 high (92%), and the description adds minor context about exchange+symbol usage. Baseline is 3, and the description does not significantly add beyond the schema.

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

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

The description clearly states the tool orchestrates multiple calculations (position sizing, breakeven, etc.) into one call, and provides specific use cases like 'should I take this trade?'. It distinguishes itself from sibling tools that focus on individual components.

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 advises when to use the tool (full trade setup) and recommends providing exchange+symbol for automatic funding rate fetch. However, it does not explicitly mention when not to use it or list alternative sibling tools.

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

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

With no annotations, the description fully discloses the tool's behavior: it calculates and returns R:R, position size, liquidation price, breakeven, P&L, and a verdict. No mention of side effects, but as a calculation tool, no destructive actions are expected.

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 purpose and key outputs. Every sentence is necessary and free of fluff, achieving maximum conciseness.

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

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

Given the tool's 10 parameters and no output schema, the description covers essential return values and verdict criteria. It omits parameter ranges or examples, but provides sufficient context for an agent to understand the tool's purpose and results.

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 high (90-100%), so baseline is 3. The description adds context by explaining outputs derived from parameters, but does not expand on individual parameter meanings beyond what the schema already provides.

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 performs full risk:reward analysis, listing specific outputs (R:R ratio, position size, liquidation price, breakeven, P&L) and a verdict. It distinguishes itself as 'the single best tool' for trades with entry, stop, and target, differentiating from sibling tools.

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

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

The description explicitly tells when to use the tool: 'when user describes a trade with entry, stop, and target' and 'when user asks "is this trade worth taking?" or "what's my risk reward on this setup?"'. It implies alternatives exist but does not explicitly list when not to use.

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

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

No annotations are provided, so the description carries the behavioral disclosure burden. It lists output fields (per-level pnl, weightedAvgExitPrice, totalRoi) and implies a read-only planning operation, but it lacks details on side effects, authentication needs, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise at three sentences, front-loaded with purpose, and includes a practical example and clear return list. No extraneous words 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?

Given no output schema, the description adequately covers return values and parameter usage through an example. However, it lacks mention of error conditions, edge cases, or limits on number of exits beyond what schema provides.

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?

With 67% schema description coverage, the description adds value by providing a usage example that clarifies the 'exits' parameter structure (array of price and pct objects). This compensates for the schema's missing descriptions on optional fee parameters and reinforces parameter 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?

Description clearly defines the tool as a scale-out planner computing P&L, ROI, and cumulative P&L for each partial exit level. Includes a concrete example showing multiple exit targets, which distinguishes it from related sibling tools like workflow.run_exit_target or workflow.run_pnl_planning.

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 ('Use when user wants to take profit at multiple targets'). However, it does not mention when not to use or explicitly contrast with alternatives, leaving some ambiguity.

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

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It implies a pure computation by saying 'compute PnL', but it never explicitly states that this is read-only, does not place orders, or has no side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and directly structured: purpose, example trigger, and return shape. It avoids unnecessary detail while covering the essential information an agent needs.

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 moderate complexity and absence of an output schema, the description appropriately includes the return array structure. It does not discuss edge cases, but the provided example and schema descriptions are sufficient for typical use.

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 most parameters with descriptions, and the tool description adds useful context by defining the return fields. The 'side' parameter lacks a description, but its enum values 'long'/'short' are self-explanatory, so the overall parameter meaning is well conveyed.

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

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

Description clearly states the tool's purpose: 'Run a scenario analysis: compute PnL for multiple price-change percentages at once.' It differentiates from sibling workflows by emphasizing multiple price-change scenarios in one call, and gives a concrete example user query.

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 an explicit trigger example: 'Use when user asks "show me my P&L if BTC moves -10%, -5%, +5%, +10%"' and explains the return shape. It does not explicitly name sibling alternatives or state when not to use this tool, but the usage context is clear enough.

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

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

No annotations are provided, so the description carries full burden. It details the algorithm sources (TPO, initial balance, etc.) and the output categories, but does not explicitly state safety (e.g., read-only, no side effects) or any limitations. Adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences: the first explains what the tool does and its output, the second provides a usage question. No redundant information, perfectly front-loaded.

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

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

With 7 parameters all documented in schema and no output schema, the description lists the possible day-types, which is helpful. It lacks details on return structure or error conditions, but the given information is sufficient for most use cases.

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

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

Schema description coverage is 100%, with each parameter well-documented (e.g., venue enum, candles optional, timeframe defaults). The tool description adds minimal parameter insight beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states it is a 'Market Profile day-type classifier' and lists the specific categories (trend, balance, neutral_trend, normal, normal_var). It distinguishes from siblings by focusing on day-type classification using TPO, initial balance, etc., which is unique among the workflow tools.

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 provides a usage question: 'Use for "is this a trend day or a balance day?"', giving clear context. While it does not mention when not to use or alternatives, the explicit usage case suffices for a 4.

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 must disclose behavior. It explains the computation (value-area migration) but does not mention that if 'candles' are not supplied, the tool fetches data from the venue (which may involve latency or costs). The description hints at the output (direction) but omits side effects or auth requirements.

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 with no wasted words. It front-loads the core concept ('Market Profile value-area migration across sessions') and ends with a practical use case. 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 should convey the return value. It implies the output is a directional assessment ('up, down, or overlapping') but does not specify format or additional details. With 8 parameters, the description lacks guidance on parameter interactions or workflow steps, leaving gaps for an agent.

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

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

Schema coverage is 100% with each parameter having a clear description. The tool description adds no parameter-specific details beyond the schema, so the baseline score of 3 applies. The description provides context for the 'lookback_sessions' parameter by mentioning 'across sessions', but no extra semantic meaning.

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 analyzes Market Profile value-area migration across sessions to determine direction (up/down/overlapping). It provides a concrete use case question, 'is value moving higher day over day?', making the tool's specific purpose unambiguous and distinguishable from sibling workflow tools.

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 gives a clear use case ('Use for is value moving higher day over day?'), implying when to apply the tool. It does not explicitly exclude alternatives or contrast with sibling tools, but the context of multi-session migration is sufficiently clear for an agent to infer appropriate usage.

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