openmm-mcp

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. It states the action but does not mention that the operation is irreversible, what happens if there are no open orders, whether it cancels both buy and sell orders, or what response to expect. This is a significant transparency gap for a destructive operation.

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. Every word contributes to understanding the tool's purpose and scope.

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 only two fully documented parameters and no output schema, the description is minimally viable for invoking the tool. However, the absence of annotations and the lack of any mention of return values, safety, or side effects leaves notable contextual gaps for an operation that cancels all open orders.

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%, and both parameters are already well described with format and supported values. The tool description adds no semantic meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

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

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

The description states a specific verb ('Cancel'), a specific resource ('all open orders'), and the scope ('for a trading pair on a supported exchange'). This clearly differentiates it from sibling cancel_order, which implies a single order cancellation.

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 makes the intended use case clear: bulk cancellation of all open orders for a pair. However, it does not explicitly reference the sibling cancel_order tool or state when to choose one over the other, so usage guidance 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?

The tool has no annotations, so the description bears the full disclosure burden, yet it only says 'Cancel', a mutation, without stating whether cancellation is irreversible, idempotent, or what happens to a filled/canceled order. Permissions, confirmation, and error behavior are not 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?

A single sentence with no filler; the key qualifier 'specific' and 'by ID' are up front, and the exchange constraint is stated once. 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 simple three-parameter mutation, the description conveys the core operation, and the schema fills in parameter details. However, with no annotations and no output schema, it omits behavioral context like response shape, failure modes, and cancellation guarantees, making it minimally viable.

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

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

The input schema already documents all three parameters (exchange, symbol, orderId) at 100% coverage, so the description need not repeat them. It adds a slight framing with 'by ID' but no new semantic detail, matching the 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?

States the specific action (cancel), the exact target (an order identified by ID), and the scope (a single order on a supported exchange). This clearly distinguishes it from sibling cancel_all_orders, which targets multiple orders.

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 'a specific order by ID' establishes the primary use case: canceling one known order. It does not explicitly mention when to use cancel_all_orders or other alternatives, but the context is clear enough for an agent to select it for individual cancellations.

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 responsibility for disclosing behavior. It only states that an order is created but does not mention execution side effects, exchange confirmation, irreversibility, fees, auth requirements, or what happens after the call. For a financial mutation tool, this is a significant gap.

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

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

The description is one short, front-loaded sentence with no filler or redundant elaboration. It earns its place by stating the core purpose clearly.

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

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

With no annotations and no output schema, the description needs to cover more context, such as return values, order lifecycle, or side effects. It only provides a minimal statement of intent, which is insufficient for a complex financial action like order creation.

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

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

Schema description coverage is 100%, so the schema already documents all parameters well. The description adds minimal extra meaning beyond naming limit and market order types, and it does not compensate or add detail 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 ('Create') with a clear resource ('new order') and scope ('limit or market', 'on a supported exchange'). It clearly distinguishes this tool from sibling tools like cancel_order, get_balance, and list_orders.

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 no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or situations where another tool would be more appropriate. There is no explicit when-to-use or when-not-to-use 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?

No annotations are provided, so the description carries the full burden of disclosing behavior. 'Discover' implies a read-only lookup, but the description does not state whether it modifies anything, what happens when no pools exist, whether there are rate limits, or what the response shape is. This leaves meaningful behavioral uncertainty.

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, well-structured sentence that front-loads the action and resource, then specifies the API. Every word contributes meaning, with no filler or redundancy.

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

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

For a tool with one documented parameter and no output schema, the description is concise but sufficient to understand the tool's purpose and invoke it. It could elaborate on the return format or edge cases, but the low complexity means the description is nearly complete as-is.

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%, and the schema already documents 'symbol' as a Cardano token symbol with examples. The description adds no parameter details beyond what the schema provides, so 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 uses a specific verb ('Discover') with a specific resource ('Cardano DEX liquidity pools for a native token') and identifies the integration ('Iris API'). It is immediately distinguishable from all sibling tools, which focus on orders, balances, prices, and strategies rather than pool discovery.

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 context is clear: use this tool when you need to find DEX liquidity pools for a Cardano native token. It does not explicitly name alternatives or exclusions, but none of the sibling tools perform pool discovery, so the intended use is unambiguous 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?

With no annotations, the description carries the transparency burden. It clearly indicates a read-only balance query and the asset-level filter, but it does not mention authentication requirements, rate limits, or what happens for unsupported assets. For a simple read operation this is adequate but not comprehensive.

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 focused sentence that front-loads the verb and resource, includes the optional filtering behavior, and adds no redundant or filler content.

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 low complexity, fully documented parameters, and absence of an output schema, the description is nearly complete for selecting and invoking the tool. Minor omissions like response format or authentication setup do not prevent successful use, but they would add polish.

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 both parameters (exchange and asset) are already documented with their meanings and supported values. The description adds no substantial parameter semantics beyond paraphrasing the asset filter, matching the baseline for fully covered schemas.

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 ('Get account balances') and clearly scopes the operation to all assets or a specific asset on a supported exchange. This distinguishes it from sibling tools like get_ticker or get_trades, which concern market data rather than account balances.

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 checking balances and notes it works on supported exchanges, but it does not explicitly state when to use it versus alternatives or mention any prerequisites such as authentication. The usage context is inferable but not fully articulated.

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 usefully reveals that the price is aggregated and sourced from DEX liquidity pools, but it does not mention side effects (read-only nature), failure modes, unsupported symbols, or whether the price may vary based on pool conditions.

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, information-dense sentence with no filler. The core action and key qualifiers are front-loaded, and every phrase adds meaning.

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 read tool, the description provides the essential context: what price, from where, and the pairing route. It is sufficient for basic selection and invocation, though it could be more complete by describing the expected return value or error behavior in the absence of 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 schema already fully documents the single 'symbol' parameter with example values, so schema coverage is 100%. The description adds context about the quote convention (TOKEN/USDT via ADA bridge) but does not add further parameter semantics 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 ('Get') and defines the exact resource: an aggregated price for a Cardano native token sourced from DEX liquidity pools. It also clarifies the quote path ('TOKEN/USDT via ADA bridge'), which distinguishes this tool from generic ticker or exchange price 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 this is for Cardano native token prices aggregated from DEX pools, but it does not explicitly state when to use this tool versus siblings like get_ticker or discover_pools. There is no when-to-use guidance or mention of exclusions.

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, yet it only states the basic fetch action. It does not mention whether the data is historical, how ordering or pagination behaves, what the response contains, or any rate-limits or safety characteristics, leaving an agent without useful 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 a single, front-loaded sentence with no filler or redundancy. It efficiently states the action and resource, which is an ideal size for this simple data-fetch 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?

The schema completely documents all four parameters, including defaults and constraints, and OHLCV has a standard financial meaning, so the description is functionally adequate. However, with no output schema and no behavioral notes, details like candle ordering, whether the current incomplete candle is included, and the exact response structure remain unspecified, making the definition complete only at a minimal level.

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

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

Schema description coverage is 100%, so the schema fully documents exchange, symbol, limit, and timeframe; the description adds no additional meaning beyond the schema. The phrase 'trading pair' maps to the symbol parameter but does not deepen understanding of any parameter, 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 uses a specific verb ('Get') and a specific resource ('OHLCV (candlestick) data') for a trading pair, making the tool's function clear. It implicitly differentiates from siblings like get_ticker or get_trades, but it does not explicitly contrast itself with any alternative, so it falls just short of a top score.

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

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

There is no guidance on when to use this tool versus alternatives such as get_ticker or get_trades, and no mention of use cases, prerequisites, or exclusions. The only implied usage is that an agent should call it when candlestick data is requested, but this is left entirely to inference.

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

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

With no annotations provided, the description carries the full disclosure burden. 'Fetch' correctly signals a read-only operation, and 'bids and asks' hints at the response shape. However, it omits details such as whether the data is a live snapshot, how levels are aggregated, or any exchange-specific quirks. For a simple read tool this is adequate but not rich.

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 that states the action and the resource with zero filler. Every word contributes to understanding the tool's purpose.

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 core purpose, and the schema fully documents the parameters and constraints. Although there is no output schema, the phrase 'bids and asks' conveys the essential return structure. Missing details like price/quantity field ordering are minor for this simple read 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 schema already documents each parameter with types, defaults, constraints, and an exchange whitelist. The description adds no parameter-specific meaning, which is acceptable since the schema carries the full semantic load.

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 the specific verb 'Fetch' and names a concrete resource: 'order book depth (bids and asks)' for a trading pair. This clearly distinguishes it from sibling tools like get_ticker, get_trades, and get_ohlcv, which all target different market data dimensions.

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 liquidity/depth data, but it does not explicitly state when to prefer it over alternatives or exclude other data types. An agent can infer the use case from the phrase 'order book depth', but no direct comparison to sibling tools is provided.

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 disclosure burden. It clearly signals a read-only operation via 'Get' and discloses the output components (open orders, current price, P&L estimate). However, it does not state preconditions such as whether an active strategy must exist for the given symbol/exchange, what happens when no strategy is found, or whether the price is a live snapshot or cached value.

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 zero filler. The verb, resource, and the three returned components each earn their place, and nothing is redundantly repeated from the input schema or the rest of the definition.

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 is low-complexity (two flat params, no output schema, no annotations), and the description partially compensates for the missing output schema by listing what the call returns. The remaining gap is behavioral context: what occurs if no strategy is running on the queried exchange/symbol, and whether the strategy must be started before this call is valid.

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 both symbol and exchange already documented with examples and supported values. The description adds only implicit context that these parameters identify which grid strategy to query, but introduces no format details, ranges, or validation rules beyond what the schema already provides. Baseline 3 applies.

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

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

The description uses a specific verb ('Get') with a distinct resource ('grid strategy status') and enumerates the returned data (open orders, current price, P&L estimate). The resource is unique among the sibling tools — none of the other reads (get_ticker, get_balance, get_trades) target strategy state — so it can be told apart without opening the schema, though it does not explicitly name alternatives.

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

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

The use case is implied: call this to inspect the state of a grid strategy for a given exchange/symbol. However, there is no explicit when-to-use guidance, no mention of when not to use it (e.g., when a strategy is not running), and no named alternatives such as start_grid_strategy for lifecycle operations or get_ticker for live market price.

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 adds 'real-time' and lists the returned metrics, which gives a basic behavioral profile. However, it doesn't disclose error behavior, rate limits, data freshness guarantees, or whether the operation has side effects, though the name suggests it is 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 a single compact sentence that front-loads the core action and then specifies the exact returned fields. Every word earns its place; there is no filler or repetition of schema 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 low-complexity two-parameter read tool, the description plus full schema coverage is sufficient for an agent to interpret what the tool returns and what inputs it needs. The returned metrics are enumerated, which partially compensates for the missing output schema. A small gap remains around when to prefer this over sibling tools.

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 both parameters having meaningful descriptions and examples, including supported exchanges for the exchange parameter. The description adds no parameter-specific detail, but the schema already handles that burden, so the baseline 3 applies.

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

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

The description uses a specific verb ('Get') and resource ('ticker'), then enumerates exactly what is returned: real-time price, bid/ask, spread, and volume. This clearly differentiates it from siblings like get_ohlcv and get_orderbook without needing to open the schema.

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

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

Usage is implied rather than explicit. The tool name and return fields suggest it is for current market snapshots, and sibling names like get_ohlcv hint at alternatives, but the description never states when to use get_ticker versus another tool or when not to use it.

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 reveals only that trades are 'recent' and does not mention ordering, result limits, response shape, or any side effects; the read-only nature is only implied by the word 'Get'.

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 clear sentence with no wasted words. It front-loads the core action and resource immediately.

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 annotations, output schema, and usage guidance, the description is too thin to fully support an agent operating among many sibling market-data tools. It does not mention what a returned trade looks like, how recent is defined, or how this differs from related endpoints.

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

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

Schema description coverage is 100%, so the schema already documents exchange, symbol, and limit with examples and constraints. The description adds no parameter meaning beyond what the schema provides, which matches the baseline of 3.

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

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

The description states a clear verb-resource pair: 'Get recent trades' for a 'trading pair'. The resource 'trades' is distinct from sibling tools like get_orderbook or get_ticker, though it does not explicitly name or contrast any sibling.

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

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

No guidance is provided about when to use this tool versus alternatives such as get_orderbook, get_ticker, or list_orders. The description does not mention exclusions, prerequisites, or use cases, so the agent receives no routing help.

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 carry the behavioral burden. It clearly indicates a read-only enumeration and names the output fields, but it does not disclose pagination, data freshness, authentication expectations, or whether the list is static or dynamic.

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 sentence with no filler. It front-loads the action and object and packs relevant output details into a compact, readable format.

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 list operation, the description is largely complete: it names the resource and the returned fields. It only lacks slight guidance on when the tool should be invoked and does not describe the exact response structure, which would be useful given there is no 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 tool has zero parameters and the input schema is empty, so there is no parameter documentation burden. The baseline 4 applies because there is nothing for the description to add beyond what an empty schema already shows.

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-resource pair, 'List all supported exchanges,' and enumerates the returned content: IDs, features, and required credentials. This makes its purpose clear and distinct from the order/strategy 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 does not state when to use this tool versus alternatives or mention any prerequisites. While no sibling is dedicated to listing exchanges, the description offers no guidance about whether to call this before configuring credentials or setting up strategies.

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 not mention authentication requirements, rate limits, pagination, or whether the operation is strictly read-only. The verb 'List' implies a read operation, but important behavioral details are omitted.

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, well-structured sentence that front-loads the core purpose and then adds the optional filter. Every token earns its place; 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?

For a simple two-parameter tool, the description and schema cover the essential inputs and purpose. However, with no output schema and no annotations, the description does not explain the return format, authentication requirements, or any exchange-specific behavior, leaving some gaps for an agent to resolve.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds little beyond the schema, only restating the optional symbol filter. Baseline 3 is appropriate since the description does not need to compensate.

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') with a clear resource ('open orders') and context ('on a supported exchange'), and mentions the optional symbol filter. This clearly distinguishes it from sibling tools like cancel_order, create_order, and get_trades.

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 usage is implied: use this tool to list currently open orders on an exchange, optionally filtered by pair. However, there is no explicit guidance on when not to use it or how it differs from alternatives such as get_trades or get_orderbook, 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?

With no annotations, the description carries the burden of conveying side effects. It does disclose that order placement is optional, which is an important safety-relevant behavior. However, it does not explain what happens when dryRun is false, how many orders may be placed, or what result the agent should expect.

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 efficient sentence with no filler. The key verbs 'calculate' and 'optionally place' are front-loaded, and the 'around the current price' scope is immediately clear.

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

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

Despite having 9 parameters, potential order placement, no annotations, and no output schema, the description provides almost no operational context. It does not explain how the grid strategy is constructed, what the preview output contains, or what side effects occur when orders are actually placed.

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

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

Schema description coverage is 100%, so the schema already documents each parameter. The description adds no deeper meaning about how levels, spacing, sizeModel, or spacingModel interact. This meets the baseline but does not exceed it.

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

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

The description states a specific action: calculate and optionally place grid trading orders, anchored around the current price. This distinguishes it from generic order tools like create_order and from strategy lifecycle tools like get_strategy_status and stop_strategy.

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 is given for when to use this tool versus alternatives such as create_order or the strategy status/stop tools. The 'optionally place' wording hints at preview vs. execution, but it does not explicitly state when dryRun should be used or when a different tool is more appropriate.

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

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

With no annotations provided, the description carries the full burden of disclosing behavior. It clearly states the destructive scope (cancelling all open orders for a pair) and the downstream effect (stopping a running grid strategy). It does not discuss reversibility, but the main behavioral traits are 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?

A single sentence with no filler. The action and object are front-loaded followed by the intended effect, making it efficient and easy to parse.

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 two-parameter cancel operation, the description covers the action, scope, and outcome. However, given the existence of a sibling tool with an identical action ('cancel_all_orders'), the description lacks routing context to help an agent select correctly between them.

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 both 'symbol' and 'exchange' are already well documented. The description adds no additional parameter-level meaning, making the baseline score of 3 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 uses a specific verb and resource: 'Cancel all open orders for a trading pair' and adds the intended outcome of stopping a grid strategy. However, it does not differentiate itself from the closely named sibling 'cancel_all_orders', which appears to describe the same action on the same resource.

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 'effectively stopping any running grid strategy' implies the intended use case for halting a strategy. But it provides no explicit guidance about when to choose this tool over alternatives such as cancel_order or cancel_all_orders.

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