openmm-mcp

Server Quality Checklist

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Latest release: v1.0.5
Disambiguation4/5
Most tools have distinct purposes, such as create_order for order creation and get_ticker for price data. However, cancel_all_orders and stop_strategy both involve canceling orders for a trading pair, which could cause confusion in selection, though stop_strategy is tied to grid strategies.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern with snake_case throughout, such as get_balance and list_orders. Minor deviations exist, like discover_pools using 'discover' instead of 'get' or 'list', but overall the pattern is clear and readable.
Tool Count5/5
With 13 tools, the count is well-scoped for a trading and strategy automation server. Each tool serves a specific function, from order management to price fetching and strategy control, without feeling excessive or insufficient for the domain.
Completeness4/5
The toolset covers core trading operations like order CRUD, balance checks, and market data, plus grid strategy management. A minor gap is the lack of tools for modifying existing orders (e.g., update_order) or detailed strategy configuration, but agents can work around this with the available tools.
Average 3.1/5 across 13 of 13 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on March 8, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
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 states the action ('cancel') which implies a destructive mutation, but doesn't disclose critical traits like whether cancellation is reversible, what permissions or authentication are required, potential rate limits, error conditions (e.g., if order doesn't exist), or what happens upon success (e.g., order status change). This leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action ('cancel a specific order') and includes essential context ('by ID on a supported exchange'). There is no wasted verbiage, repetition, or unnecessary details, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a destructive mutation with 3 parameters) and the lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like side effects, error handling, or return values, which are crucial for safe and effective use. The description alone is insufficient for an agent to fully understand how to invoke and interpret results from this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with all three parameters ('exchange', 'symbol', 'orderId') well-documented in the schema itself. The description adds no additional parameter semantics beyond what the schema provides (e.g., it doesn't clarify parameter relationships or usage examples). According to the rules, with high schema coverage (>80%), the baseline is 3 even without param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('cancel') and resource ('a specific order by ID on a supported exchange'), making the purpose immediately understandable. It distinguishes from siblings like 'cancel_all_orders' by specifying 'specific order by ID', though it doesn't explicitly mention all alternatives like 'list_orders' for checking order status. The description avoids tautology by not just repeating the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing order ID from 'list_orders' or 'create_order'), when not to use it (e.g., for non-cancelable orders), or how it differs from 'cancel_all_orders'. Usage is implied by the action but lacks explicit context for selection among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions the tool creates orders but fails to describe critical behaviors like authentication needs, rate limits, whether the order is executed immediately, error handling, or what the response contains. This leaves significant gaps for an agent to understand how to use it effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action ('Create a new order') and includes essential details (order types and context) without any wasted words. Every part of the sentence contributes directly to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a financial order creation tool with no annotations and no output schema, the description is insufficient. It lacks details on authentication, execution behavior, error cases, and return values, which are critical for an agent to invoke this tool correctly in a trading context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 all parameters. The description adds no additional meaning beyond what's in the schema, such as explaining parameter interactions (e.g., 'price' is ignored for market orders, which is already in the schema). Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create a new order') and specifies the resource ('order') with types ('limit or market') and context ('on a supported exchange'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'cancel_order' or 'list_orders' beyond the creation aspect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'cancel_order' or 'list_orders', nor does it mention prerequisites such as authentication or balance requirements. It only states what the tool does without contextual usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but lacks behavioral details. It doesn't disclose if this is a read-only operation, potential rate limits, error conditions, or what the output might look like (e.g., list of pools with details). The description is minimal and adds little 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the key information (action and resource) and avoids unnecessary details, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool that likely returns complex data (e.g., pool listings). It doesn't explain return values, error handling, or behavioral traits, leaving significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 the single parameter 'symbol' with examples. The description adds no additional meaning beyond implying it's for a native token, which is redundant with the schema's examples. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Discover') and resource ('Cardano DEX liquidity pools for a native token'), specifying it operates via the Iris API. It distinguishes from siblings like get_orderbook or get_ticker by focusing on pools rather than orders or prices, though it doesn't explicitly contrast with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing a token symbol, or differentiate from similar tools like get_ticker, leaving the agent to infer usage based on the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool retrieves status information, implying a read-only operation, but does not cover critical aspects like authentication requirements, rate limits, error conditions, or the format of the returned data (e.g., JSON structure). This leaves significant gaps for an agent to understand how to handle the tool effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose. It avoids unnecessary words, but could be slightly improved by structuring key points (e.g., listing the retrieved data more clearly) for better readability, though it remains highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a grid strategy tool with no annotations and no output schema, the description is incomplete. It lacks details on the return format (e.g., what 'P&L estimate' includes), error handling, and dependencies on other tools like 'start_grid_strategy'. This makes it inadequate for an agent to fully understand the tool's context and usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema fully documents the parameters (exchange and symbol). The description adds no additional meaning beyond what the schema provides, such as explaining how the symbol relates to the grid strategy or any constraints on the exchange-symbol combination. Thus, it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and the resource ('current grid strategy status'), specifying what information is retrieved (open orders, current price, P&L estimate). However, it does not explicitly differentiate from sibling tools like 'list_orders' or 'get_ticker', which might provide overlapping or related data, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, such as needing an active grid strategy, or compare it to siblings like 'list_orders' for order details or 'get_ticker' for price data, leaving usage context implied at best.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'recent trades' but doesn't specify what 'recent' means (e.g., time window, pagination), whether there are rate limits, authentication requirements, or what format the results will be in. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with no wasted words. It's appropriately sized for a straightforward data retrieval tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data retrieval tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'recent' means, the structure of returned trade data, potential errors, or how it differs from sibling tools. Given the complexity of trading data and lack of structured output information, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 three parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema, maintaining the baseline score for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get recent trades') and resource ('for a trading pair on a supported exchange'), making the purpose immediately understandable. It doesn't explicitly differentiate from siblings like 'get_orderbook' or 'get_ticker', but the focus on trades is specific enough to avoid confusion with those alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like 'get_orderbook' or 'get_ticker', nor does it mention prerequisites or exclusions. It simply states what the tool does without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'optionally place' orders and implies a 'dryRun' parameter, but doesn't disclose critical behavioral traits such as authentication requirements, rate limits, whether orders are placed immediately or queued, error handling, or what happens if the strategy fails. This is inadequate for a complex trading tool with potential financial implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality. It avoids redundancy and wastes no words, though it could be slightly more structured by separating calculation from placement aspects.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, trading functionality) and lack of annotations or output schema, the description is insufficient. It doesn't explain what the tool returns (e.g., a strategy ID, order details, or just a confirmation), error conditions, or how it integrates with other tools like 'get_strategy_status'. This leaves significant gaps for an AI agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the schema already documents all 9 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'spacing' interacts with 'spacingModel' or practical examples of 'orderSize'. The baseline of 3 is appropriate given the comprehensive schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Calculate and optionally place grid trading orders around the current price.' It specifies the verb ('calculate and optionally place') and resource ('grid trading orders'), though it doesn't explicitly differentiate from sibling tools like 'create_order' or 'stop_strategy' beyond the 'grid' concept.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'create_order' for single orders or 'stop_strategy' for stopping strategies, nor does it specify prerequisites or appropriate contexts for grid trading.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 tool retrieves balances but doesn't mention critical behaviors such as authentication requirements, rate limits, whether it's a read-only operation, or what the output format looks like (e.g., JSON structure). For a financial tool with no annotation coverage, this leaves significant gaps in understanding how it behaves beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get account balances') and includes key details (scope and filtering) without unnecessary words. Every part of the sentence earns its place by clarifying the tool's functionality, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a financial balance tool with no annotations and no output schema, the description is incomplete. It lacks information on authentication, rate limits, error handling, and the structure of returned data. While it covers the basic purpose, it doesn't provide enough context for safe and effective use in a real-world scenario, especially compared to siblings that might have similar gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 (exchange and asset) with clear descriptions. The description adds marginal value by reinforcing that asset is optional and specifying 'all assets if omitted,' but this is largely redundant with the schema. Baseline score of 3 is appropriate as the schema does the heavy lifting, and the description doesn't add significant new semantic details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('account balances for all assets or a specific asset on a supported exchange'). It distinguishes from siblings like get_ticker or get_orderbook by focusing on balances rather than market data or orders. However, it doesn't explicitly differentiate from all siblings (e.g., get_strategy_status might also involve account information).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'all assets (or a specific asset)' and 'on a supported exchange,' which suggests when to use it for balance queries. However, it doesn't provide explicit guidance on when to choose this tool over alternatives (e.g., vs. list_orders for order-related balances) or any prerequisites like authentication needs. The context is clear but lacks detailed alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral disclosure. It mentions 'aggregated price from DEX liquidity pools' which implies read-only data fetching, but doesn't cover rate limits, data freshness, error conditions, authentication needs, or what 'aggregated' means computationally. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, dense sentence with zero wasted words. Every element ('Get aggregated price', 'Cardano native token', 'DEX liquidity pools', 'TOKEN/USDT via ADA bridge') serves a specific informational purpose. It's front-loaded with the core purpose and efficiently conveys the essential details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a single parameter with good schema coverage, the description is incomplete. It doesn't explain what the aggregated price represents (weighted average? median?), how frequently it updates, what format the return value takes, or potential error scenarios. For a financial data tool, this leaves too much unspecified about the tool's behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage for its single parameter, so the baseline is 3. The description adds marginal value by listing example token symbols (INDY, SNEK, MIN, NIGHT) which helps clarify the 'symbol' parameter's expected values, but doesn't provide additional semantic context beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get aggregated price') and resource ('Cardano native token'), specifying the exact mechanism ('from DEX liquidity pools') and context ('TOKEN/USDT via ADA bridge'). It distinguishes itself from sibling tools like get_ticker or get_orderbook by focusing on aggregated token pricing rather than market data or order books.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like get_ticker (which might provide broader market data) or get_orderbook (which shows depth). It mentions the specific token types (INDY, SNEK, MIN, NIGHT) but doesn't explain why this tool is preferred for aggregated pricing over other price-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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions 'supported exchange' but doesn't specify rate limits, authentication requirements, error handling, or response format. For a financial data tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for what it communicates. Every word earns its place in conveying the essential function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a financial data tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'bids and asks' data looks like, how results are structured, whether there are exchange-specific behaviors, or what authentication might be required. The context demands more comprehensive guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain what 'order book depth' means in practice or provide context about the limit parameter's effect on data granularity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('fetch'), resource ('order book depth'), and scope ('for a trading pair on a supported exchange'), distinguishing it from siblings like get_ticker (price only) or get_trades (historical trades). It precisely communicates what the tool does without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose get_orderbook over get_ticker for price data, or how it differs from list_orders for order information. There's no context about prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states it lists open orders with optional filtering. It doesn't disclose behavioral traits such as rate limits, authentication requirements, pagination, response format, error conditions, or whether it includes partially filled orders. For a read operation in a trading context, this leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('List open orders on a supported exchange') and adds necessary qualification ('optionally filtered by trading pair symbol'). There is no wasted verbiage, and every word earns its place in conveying essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a trading tool with no annotations and no output schema, the description is incomplete. It doesn't explain what data is returned (e.g., order IDs, types, statuses, timestamps), how results are structured, or any limitations (e.g., time ranges, maximum orders returned). For a tool interacting with financial exchanges, this lack of detail could lead to incorrect usage by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear documentation for both parameters in the input schema. The description adds marginal value by reinforcing the optional nature of the symbol filter ('Returns all if omitted'), but doesn't provide additional semantic context beyond what's already in the schema descriptions, such as format examples beyond 'BTC/USDT' or exchange-specific nuances.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and resource 'open orders on a supported exchange', with optional filtering by trading pair symbol. It distinguishes from siblings like 'cancel_order' or 'create_order' by focusing on retrieval rather than modification, but doesn't explicitly differentiate from other read operations like 'get_orderbook' or 'get_trades'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through the phrase 'optionally filtered by trading pair symbol', suggesting this tool is for viewing orders with optional filtering. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'get_orderbook' for market depth or 'get_trades' for completed transactions, nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 ('cancel') but does not describe what 'cancel' entails (e.g., immediate vs. queued, irreversible effects), authentication requirements, rate limits, error conditions, or response format. For a mutation tool with zero annotation coverage, this is a significant gap 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action and scope without unnecessary words. Every part of the sentence contributes directly to understanding the tool's purpose, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation operation with potential side effects), lack of annotations, and no output schema, the description is incomplete. It does not address critical aspects like what happens after cancellation (e.g., confirmation, error handling), security requirements, or limitations, leaving significant gaps for an AI agent to understand the tool fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('exchange' and 'symbol') well-documented in the schema. The description does not add any additional meaning beyond what the schema provides (e.g., no extra details on exchange support or symbol formats). Baseline 3 is appropriate as the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('cancel all open orders') and target resource ('for a trading pair on a supported exchange'), distinguishing it from sibling tools like 'cancel_order' (which likely cancels individual orders) and 'list_orders' (which lists rather than cancels). The verb 'cancel' is precise and the scope 'all open orders' is explicitly defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when needing to cancel all open orders for a specific trading pair and exchange, but it does not explicitly state when to use this tool versus alternatives like 'cancel_order' or provide exclusions (e.g., no guidance on prerequisites such as authentication or rate limits). The context is clear but lacks explicit alternatives or when-not-to-use details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'real-time' data, which is useful context, but fails to disclose other critical traits such as rate limits, authentication requirements, error handling, or data freshness guarantees. For a read operation with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently conveys the tool's purpose without any wasted words. Every part of the sentence earns its place by specifying the data retrieved and the context (supported exchange), making it appropriately sized and structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, no nested objects) and 100% schema coverage, the description is adequate but not complete. It lacks output schema, so return values are undocumented, and with no annotations, behavioral aspects like rate limits or errors are missing. For a simple read tool, it meets minimum viability but has clear gaps in contextual information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters (exchange and symbol) well-documented in the schema. The description adds no additional meaning beyond what the schema provides (e.g., it doesn't explain format nuances or constraints). According to the rules, with high schema coverage, the baseline is 3 even without param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get') and resource ('real-time price, bid/ask, spread, and volume for a trading pair'), distinguishing it from siblings like get_balance (balances), get_orderbook (order book data), and get_trades (trade history). It precisely defines what data is retrieved without being vague or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying 'on a supported exchange,' which provides some context for when to use this tool (for real-time market data on those exchanges). However, it does not explicitly state when to use it versus alternatives like get_orderbook (for depth data) or get_cardano_price (for a specific asset's price), nor does it mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks critical behavioral details. It mentions cancellation but doesn't disclose whether this is reversible, requires specific permissions, has rate limits, or what happens to partially filled orders. The 'effectively stopping' phrasing adds some context about strategy impact, but key operational traits are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence efficiently conveys the core action, target, and strategic effect with zero redundant information. Every word earns its place, and the structure is front-loaded with the primary purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive operation with no annotations and no output schema, the description is minimally adequate. It covers the 'what' but lacks details about consequences, error conditions, or return values. Given the complexity of stopping trading strategies, more behavioral context would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are fully documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides (exchange and symbol usage). Baseline 3 is appropriate when schema handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Cancel all open orders') and resource ('for a trading pair'), with explicit mention of the effect on 'any running grid strategy'. It distinguishes from sibling tools like 'cancel_all_orders' by specifying the strategic context and trading pair scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('effectively stopping any running grid strategy'), suggesting it's for halting automated strategies rather than general order cancellation. However, it doesn't explicitly state when NOT to use it or name alternatives like 'cancel_all_orders' for non-strategic scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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