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

No annotations are provided, so the description carries the full burden. The description clarifies the scope (only backtest jobs, not orders), which is critical behavioral context. However, it does not mention side effects such as whether the cancellation is irreversible or if resources are freed, but given the tool is a cancellation, the key transparency is the scope distinction, which is well covered.

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

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

The description is a single sentence that is concise and front-loaded with the core purpose ('Cancel a running research backtest'), followed by an important clarification. There is zero 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?

For a simple cancel operation with one well-documented parameter, the description effectively covers the core usage. The only minor gap is not specifying what happens after cancellation (e.g., no return value description), but given the lack of output schema and the simplicity, it is reasonably complete.

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

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

The schema has 100% coverage for the single parameter, describing it as 'Backtest ID returned by tastytrade.' The description does not add extra semantics beyond the schema, but since the schema is complete, the baseline of 3 is appropriate.

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

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

The description clearly states the verb (cancel), the resource (a running research backtest), and explicitly distinguishes it from brokerage orders. This makes it unambiguous and differentiates it from sibling tools like tastytrade_list_backtests or tastytrade_create_backtest.

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

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

The description explicitly states when to use this tool (to cancel a running backtest) and what it does NOT affect (never a brokerage order). This is a clear 'when/not' guideline, which is particularly important given the presence of tastytrade_simulate_trade and other order-related tools in 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?

With no annotations, the description carries the disclosure burden and does meaningfully reveal that no brokerage orders are placed, which is a non-obvious and important behavioral trait. Still, it does not mention whether the backtest is executed asynchronously, how long it may take, or what the response represents.

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 short sentences with no filler. It front-loads the action and resource, then immediately states the key caveat, making it easy for an agent to parse quickly.

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 adequate for a thin API wrapper, especially with sibling names like tastytrade_get_backtest and tastytrade_cancel_backtest hinting at the lifecycle. However, it does not explain what the tool returns or that the backtest may run asynchronously, which would help an agent know how to poll or retrieve 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?

There is only one parameter and the schema already describes it thoroughly as the raw POST /backtests request body. The tool description itself adds no parameter-level guidance, so it stays at the baseline for full schema coverage. The provider-native note in the schema is helpful but is already part of the structured input definition.

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 action ('Create') and the resource ('a tastytrade historical options backtest'), so the core purpose is unambiguous. It also adds a research-only qualifier that helps separate it from trading/order tools, though it does not explicitly differentiate from the sibling tastytrade_create_spx_spread_backtest.

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 phrase 'This is research-only and does not place brokerage orders' gives a useful boundary for when not to use it, implying it is for study rather than execution. However, it provides no explicit guidance about when to choose this tool over alternatives like tastytrade_create_spx_spread_backtest or tastytrade_simulate_trade.

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, and it does meaningful work by revealing that this is an aggregate/approximate backtest rather than exact simulation, and that double diagonals are excluded. It does not fully describe the job lifecycle or return behavior, but it discloses the most important behavioral limitation.

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 short sentences with no filler. The primary use condition is front-loaded, and the exclusion is stated immediately after. Every sentence contributes selection or invocation guidance.

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 strong for tool selection but weaker for invocation and post-creation expectations. There is no output schema, no annotations, and a complex nested request object, so the description should say more about what happens after the job is created and how results are retrieved. The double-diagonal guardrail is helpful but not sufficient for full contextual 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. The phrase 'relative leg selectors' adds meaning to the backtest_selector concept inside the request, and the aggregate-vs-exact distinction helps interpret the family field. However, it does not explain required fields like intended_price, price_effect, entry_at, or exit_at, leaving a meaningful 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?

The description names a specific action ('Create'), a specific resource ('aggregate SPX spread Backtester job'), and the key mechanism ('relative leg selectors'). It also distinguishes this tool from exact-simulation tools by declaring that double diagonals are not supported here.

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 a clear when-to-use condition: use this when the structure is faithfully representable by relative leg selectors. It also gives an explicit when-not: double diagonals are exact-simulation only. However, it does not name the exact-simulation sibling tool directly, and 'faithfully enough' is somewhat subjective.

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 disclosure burden. It discloses that the response includes current status and results only when completed, which is meaningful. However, it does not mention error behavior, whether the operation is read-only, or what happens for an unknown ID.

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, front-loaded sentence with no wasted words. It immediately identifies the action, the resource, and the key output characteristics.

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

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

For a single-parameter getter with no output schema, the description provides a useful summary of what the tool returns. It is slightly sparse on edge-case behavior, but the core calling context is sufficiently covered.

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 the 'id' parameter already described as 'Backtest ID returned by tastytrade.' The tool description adds no additional parameter meaning, so the schema does the heavy lifting and the baseline of 3 applies.

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

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

The description states a specific verb ('Get'), a resource ('a tastytrade backtest'), and the identifier-based access method ('by ID'). It also clarifies what the tool returns ('current status and results when completed'), distinguishing it from list and create 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 description clearly implies the tool is used when you already have a backtest ID and want its status or results. It does not explicitly name alternatives or provide exclusion criteria, but the 'by ID' framing gives enough context to differentiate it from listing or creating backtests.

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. The word 'List' implies a read-only operation, and the mention of date ranges adds useful context, but the description does not disclose output structure, pagination, or any other behavioral details beyond the core action.

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 short sentences with no filler. The core action and subject are front-loaded, and the usage guidance follows naturally.

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

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

For a zero-parameter, no-output-schema tool, the description is complete: it states the resource, the information returned, and the intended workflow context. An agent has enough to call it 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?

The tool has zero parametersampions and schema coverage is 100%, so there is nothing for the description to clarify about inputs. The baseline for zero-parameter tools is 4, and the description adequately describes what the tool returns.

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 lists tastytrade Backtester symbols and their available historical date ranges, which is a distinct resource from sibling tools like tastytrade_list_backtests. The verb 'List' is specific and the subject is concrete.

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 tells agents to use this before regression tests to verify symbol coverage, giving a clear intended context. It does not explicitly name alternatives or when not to use it, but for a zero-parameter listing tool the guidance is sufficient.

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 behavioral disclosure. 'Get' implies a read operation, but the description does not mention absence of side effects, output format, pagination, or error 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 a single sentence with no wasted words. The core action and resource are front-loaded, making it easy to scan.

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 one-parameter retrieval tool, the description is minimally viable: it names the input and the expected resource. However, without annotations or an output schema, it leaves usage context and behavioral details undisclosed.

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%: the single 'id' parameter is already described as the backtest ID returned by tastytrade. The description adds no extra meaning beyond the schema, so the baseline score of 3 is appropriate.

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

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

The description states a specific verb ('Get') and resource ('tastytrade Backtester execution logs') for a given 'backtest ID'. This is clear and accurate, though it does not explicitly differentiate itself from the sibling tastytrade_get_backtest tool beyond the word 'logs'.

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 such as tastytrade_get_backtest or tastytrade_list_backtests. There are no prerequisites, exclusions, or context about whether logs are available before/after a backtest completes.

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 discloses normalization, source timestamps, explicit gap warnings, and absence of resampling, which are meaningful behavioral traits. It does not cover auth, rate limits, or error behavior, but the disclosed traits are substantive.

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, tightly packed sentence with no filler. It front-loads the core purpose and then adds only high-value caveats: source timestamps, gap warnings, and no resampling.

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 viable but not fully complete for a tool with a nested request schema, many parameters, and no output schema. It mentions return-related characteristics like source timestamps and gap warnings, but omits output structure, pagination, error behavior, and session timezone semantics.

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 for parameter semantics, but it only hints at 'exact UTC' and 'session window.' It does not explain interval units, instrument_type values, timeout_ms, max_candles, or session object details, leaving much of the parameter meaning to inference.

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

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

The description opens with a specific verb and resource: 'Retrieve normalized historical OHLCV candles from tastytrade DXLink.' It further distinguishes this tool from siblings by emphasizing exact UTC/session windows, source timestamps, gap warnings, and no resampling, making its role unambiguous.

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

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

The description gives clear context on when to use this tool: when an exact UTC and session window is needed, with normalized candles, source timestamps, and gap warnings. It does not explicitly name alternatives or exclusion criteria, but the context is strong enough to guide selection among the backtest/simulation siblings.

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?

There are no annotations, so the description must carry behavioral context. It indicates authentication scope and the 'submitted' filter, and 'List' implies a read-only operation, but it does not disclose pagination, ordering, response shape, or whether in-progress backtests are included.

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

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

A single, front-loaded sentence with no filler. It conveys the action, resource, and scope economically, which is appropriate for a zero-parameter tool.

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 parameterless list operation, the description gives enough to know what the tool returns conceptually. It lacks explicit return-field or pagination details, but with no output schema, that is a minor gap.

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

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

The tool has zero parameters and the schema is fully empty, so there is nothing for the description to explain. The baseline of 4 applies because parameter semantics are simply not applicable.

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 ('List') and resource ('backtests'), and adds a scope qualifier ('submitted for the authenticated tastytrade API grant'). This makes it easy to distinguish from sibling tools such as get_backtest, create_backtest, and get_backtest_logs.

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 intended use is implied: call this to see all submitted backtests for the grant. However, it does not explicitly state when not to use it or point to a sibling alternative, so an agent has to infer the boundary from the name.

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 is the sole behavioral source. It reveals that the tool does not submit the order and that it normalizes input for both backtester and exact-leg simulation, which is useful. However, it does not disclose whether it validates inputs, checks symbol existence, or what happens with invalid spread configurations. For a non-annotated tool, this leaves key behavioral aspects implied rather than 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 sentence that front-loads the types of spreads and the core output (backtester and exact-leg simulation requests) and ends with the critical non-submitting behavior. It is concise and accomplishes its purpose in one breath, though it could have added a brief list of outputs for even faster scanning.

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 complexity (deeply nested schema over four spread families, multiple simulation paths, and no output schema), the description is minimal. It does not mention the distinction between backtester and exact-leg simulation, nor the validation or normalization steps, nor what the return value looks like. Siblings like tastytrade_simulate_spx_spread and tastytrade_create_spx_spread_backtest exist, so an agent needs to know what this returns to use it as a prerequisite. The description is not sufficient on its own.

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 has 0% description coverage, but it is an extremely detailed schema with nested properties, enums for family/action/price_effect, and even a backtest_selector object. The description only says 'normalize' and lists the spread types, which adds minimal value beyond what the schema already shows. It doesn't explain the distinction between the backtest and exact-leg outputs, nor does it clarify the interplay of fields like backtest_selector and days_until_expiration. The schema carries most of the semantic burden, so a 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 normalizes SPX spread variants (debit/credit vertical, iron condor, double diagonal) into two specific kinds of requests (Backtester and exact-leg simulation), and explicitly notes it does not submit the trade. This distinguishes it from siblings like tastytrade_simulate_spx_spread (which submits) and tastytrade_create_spx_spread_backtest (which creates a backtest).

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 says 'normalize... without submitting it' but does not explicitly state when to use it versus alternatives like tastytrade_simulate_spx_spread (which presumably submits) or tastytrade_create_spx_spread_backtest (which creates a backtest). An agent must infer that this is a preprocessing step, but there is no explicit guidance on when to call this first versus directly calling the simulation/backtest creation 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 provided, the description carries the full burden of behavioral disclosure. It does mention 'exact decimal arithmetic' and 'provenance', which are useful computational details, but it does not disclose whether the tool performs a read-only calculation, requires live quotes, has side effects, or how it handles missing or stale data.

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, front-loaded sentence with no filler. The main action and scope appear immediately, though the dense provenance phrase adds complexity without much clarity.

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

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

The tool has a complex nested schema, no annotations, and no output schema, yet the description only covers leg count and arithmetic style. It omits the request wrapper, required fields, family semantics, and the meaning of the provenance modes, leaving the agent reliant on schema inspection for critical 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 description coverage is 0%, so the description must compensate, but it only hints at '2-4 leg' and 'native-package'. It does not explain the required request object, the family enum, the structure of legs, or the meaning of fields like references, max_quote_age_ms, and max_temporal_skew_ms.

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 action ('Price'), the resource ('option package'), and the scope ('2-4 leg'), which distinguishes it from sibling backtest and simulation tools. However, the phrase 'explicit native-package, synthetic-natural, and midpoint-reference provenance' is jargon-heavy and may obscure the core purpose for an agent.

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

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

The description implies the tool is for pricing option packages, but it gives no explicit guidance on when to choose it over alternatives like tastytrade_prepare_spx_spread or tastytrade_simulate_trade. There are no when-not-to-use conditions or references to related 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?

No annotations are provided, so the description carries full burden. It states it is a simulation and returns a contract, but does not disclose whether it is read-only, any side effects, authentication needs, rate limits, or what happens to data. The term 'stable' is vague and does not explain behavioral traits beyond the basic action.

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 sentence with no filler. It front-loads the action and output. However, it is extremely short given the complexity of the tool, but conciseness is high because every word is purposeful.

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 complex nested 'request' parameter with many required fields, and no annotations or output schema, the description is completely inadequate. An agent cannot know what to put in the request, what the contract contains, or any constraints. It is missing almost all contextual information needed to call 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 0%, so the description must explain the parameters. It mentions none of the parameters, including the required 'request' object and its fields like 'family', 'legs', 'entry_at', etc. The description adds no semantic meaning to 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?

States a specific verb 'Run' and a precise object: 'exact-leg historical simulation for a normalized SPX defined-risk spread'. Also specifies the return: 'stable regression result contract'. This is distinct from sibling tools like tastytrade_simulate_trade and tastytrade_create_spx_spread_backtest by mentioning 'exact-leg' and 'normalized'.

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?

No guidance on when to use this tool versus alternatives. Does not mention scenarios, prerequisites, or exclusions. Sibling tools like tastytrade_simulate_trade and tastytrade_create_spx_spread_backtest are not referenced, leaving the agent to infer selection.

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 of behavioral disclosure. It explicitly discloses that no brokerage order is placed and that this is research-only, which is a critical safety-related behavior. It also mentions the return of historical path/results, though it does not cover error behavior, authentication requirements, 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?

Two sentences with no wasted words. The core action and output are front-loaded, and the research-only safety note is placed clearly. 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?

The tool has no annotations, no output schema, and a single opaque 'request' parameter that is essentially a raw API body. The description provides the high-level purpose and safety profile but does not explain how to construct the request, what fields are required, or what the returned path/results will look like. An agent would likely need external API documentation to call this 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 baseline is 3. The description adds minimal meaning beyond the schema, only indicating the trade is historical and single. The 'request' parameter is still opaque ('Raw request body accepted by tastytrade POST /simulate-trade'), and the description does not compensate with additional parameter-level details.

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 ('Simulate'), a specific resource ('a single historical option trade with tastytrade Backtester'), and the expected output ('historical path/results'). It also clearly differentiates from sibling tools by emphasizing 'single' trade and 'Research-only', which distinguishes it from broader backtest creation 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 implies usage context with 'Research-only; no brokerage order is placed', but it does not explicitly state when to use this tool versus alternatives like tastytrade_create_backtest or tastytrade_simulate_spx_spread. The guidance is mostly implicit and relies on the agent inferring the difference from sibling names.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does disclose a key non-mutating behavior: 'never rewrites the live paper event,' which is important for an agent to understand side effects. It also mentions that it returns 'separate post-session evidence.' However, it does not describe other behavioral traits such as rate limits, authentication requirements, or failure modes. The description is partially transparent but leaves gaps.

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 front-loaded with the primary purpose. The two sentences have no redundancy and effectively convey the core function and a key behavioral guarantee. It is appropriately sized for a tool of moderate complexity, though it could include a bit more guidance without becoming verbose.

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 complexity (a single request object with many nested required fields, no output schema), the description is far from complete. It does not explain what 'frozen paper-order limit' means, what 'forward historical interval' implies, or how to structure the request. It also does not describe the output format or any error conditions. An agent attempting to call this tool correctly would be missing essential context, especially without annotations or an output schema.

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 description provides zero information about the 'request' parameter or its nested fields. The schema is complex with many required properties, but the description does not explain what any of them mean, how to construct a valid request, or which combinations are valid. With schema description coverage at 0%, the description must compensate, but it fails to do so. The agent would have to rely entirely on the schema, which has minimal field descriptions (only 'provider_symbol' has a description).

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 ('verify') and a precise resource ('frozen paper-order limit') within a defined context ('forward historical interval'). It also clarifies the output ('separate post-session evidence') and the non-mutating behavior ('never rewrites the live paper event'). This clearly differentiates it from sibling tools like backtest creation or simulation, which are about generating or running tests rather than verifying an existing frozen order.

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 does not provide explicit guidance on when to use this tool versus alternatives. It does not mention conditions that would select this tool over others (e.g., when you need to check if a limit was touched in a past session) nor does it name any sibling tools. The verb 'verify' and the mention of 'frozen paper-order limit' imply a verification use case, but without explicit context or exclusions, the agent is left to infer usage.

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