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 what the tool does, not how it behaves. It doesn't disclose whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or what 'detailed information' includes. The description adds minimal behavioral context beyond the basic function.

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 efficiently conveys the core purpose with relevant examples. Every word earns its place with no redundancy or unnecessary elaboration, making it easy to parse and understand 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?

For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'detailed information' includes, potential response formats, error handling, or usage constraints. While the purpose is clear, the lack of behavioral and output context leaves significant gaps for an agent to use this tool effectively.

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 fully documents the single 'id' parameter with examples. The description repeats the same examples but adds no additional semantic meaning beyond what the schema provides. This meets the baseline for high schema coverage but doesn't enhance parameter understanding.

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

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

The description clearly states the specific action ('Get detailed information') and target resource ('about a specific cryptocurrency asset by its ID'), with examples provided ('bitcoin', 'ethereum', 'cardano'). It distinguishes from sibling tools like get_assets (which likely lists multiple assets) and get_markets/get_rates (which focus on different data types).

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

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

The description implies usage when you need detailed information for a specific asset ID, but doesn't explicitly state when to use this versus alternatives like get_assets (for listing) or get_rate_by_id (for exchange rates). No exclusions or prerequisites are mentioned, leaving some ambiguity about tool selection context.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool retrieves a list with prices and market data, but it doesn't cover critical aspects such as rate limits, authentication requirements, error handling, or the format of returned data. For a read operation with no annotation coverage, 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 concise and front-loaded, with the core purpose stated first and additional details about parameters following. It consists of two sentences that efficiently convey key information without unnecessary elaboration, though it could be slightly more structured for optimal clarity.

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

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

Given the tool's complexity (a read operation with three optional parameters) and the absence of both annotations and an output schema, the description is moderately complete. It covers the purpose and parameter usage but lacks details on behavioral traits and return values, which are essential for the agent to use the tool effectively. This results in a baseline adequacy score.

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 description coverage is 100%, so the input schema already documents all three parameters with clear descriptions. The description adds marginal value by summarizing the optional filtering capabilities ('filter by search term, limit results, or offset for pagination'), but it doesn't provide additional semantic context beyond what the schema offers. Baseline 3 is appropriate here.

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

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

The description clearly states the tool's purpose: 'Get a list of cryptocurrency assets with their current prices and market data.' It specifies the verb ('Get'), resource ('cryptocurrency assets'), and key attributes ('current prices and market data'). However, it doesn't explicitly distinguish this from sibling tools like 'get_asset_by_id' or 'get_markets', which would require a 5.

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

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

The description implies usage through optional filtering parameters ('You can optionally filter by search term, limit results, or offset for pagination'), but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_asset_by_id' for single assets or 'get_markets' for market-specific data. This leaves some ambiguity for the agent.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets' data, implying a read-only operation, but doesn't address other behavioral aspects such as rate limits, error handling, authentication needs, or response format. For a tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core purpose ('Get market data for a specific cryptocurrency') and adds clarifying scope ('showing all exchange markets where it trades'). There's no wasted text, but it could be slightly more structured (e.g., by explicitly mentioning parameters) to achieve a perfect score.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and scope but lacks details on behavioral traits, usage guidelines, and output expectations. With no annotations or output schema, the description should do more to compensate, but it meets a basic threshold.

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 has 100% description coverage, with clear documentation for both parameters (baseId and limit). The description adds no additional parameter semantics beyond what the schema provides, such as examples of baseId values or details on limit behavior. With high schema coverage, 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 clearly states the tool's purpose with a specific verb ('Get') and resource ('market data for a specific cryptocurrency'), and it specifies the scope ('all exchange markets where it trades'). However, it doesn't explicitly distinguish this tool from its siblings (e.g., get_asset_by_id, get_rates), which would be needed for a score of 5.

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

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

The description provides no guidance on when to use this tool versus alternatives like get_assets or get_rates. It mentions the scope ('all exchange markets') but doesn't clarify use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name and parameters alone.

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 full burden. It clearly describes a read-only operation ('Get') but doesn't disclose behavioral traits like rate limits, authentication requirements, error handling, or response format. The description is accurate but lacks operational 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, efficient sentence that front-loads the purpose and provides clarifying examples. Every word earns its place with zero waste 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 simple read operation with one parameter and no output schema, the description is minimally complete. It covers what the tool does but lacks context about return values, error cases, or integration with sibling tools, which would be helpful given the server's financial data context.

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

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

Schema description coverage is 100%, so the schema already fully documents the 'id' parameter with the same examples. The description adds no additional parameter meaning beyond what's in the schema, meeting the baseline for high schema coverage.

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

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

The description clearly states the specific action ('Get exchange rate') and resource ('for a specific currency by its ID'), with examples provided. It distinguishes from sibling tools like 'get_rates' (likely for multiple rates) by specifying single-currency lookup.

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

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

The description implies usage context by specifying 'by its ID' with examples, suggesting this is for retrieving rates when you know the exact currency identifier. However, it doesn't explicitly state when to use this versus alternatives like 'get_rates' or 'get_markets'.

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 states it retrieves a list but doesn't specify if this is a read-only operation, whether it requires authentication, rate limits, or what the return format looks like (e.g., JSON structure, pagination). This leaves significant gaps for an agent to understand how to invoke 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.

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized for a simple retrieval tool, with zero waste.

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

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

Given the tool has no parameters, no annotations, and no output schema, the description provides a basic understanding of what it does but lacks details on behavior, return values, or differentiation from siblings. For a simple tool with zero complexity, this is adequate but has clear gaps in usage context and output expectations.

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 has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't mention any parameters, which is appropriate here since none exist. It adds no semantic detail beyond the schema, but with zero parameters, the baseline is 4 as the description doesn't need to compensate for missing param info.

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

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

The description clearly states the action ('Get a list') and resource ('exchange rates for various currencies and cryptocurrencies'), providing specific purpose. However, it doesn't differentiate from sibling tools like 'get_rate_by_id' or 'get_markets', which likely serve related but distinct functions.

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 on when to use this tool versus alternatives such as 'get_rate_by_id' (which might fetch a single rate) or 'get_markets' (which might involve market data). The description implies a broad retrieval but lacks explicit context or exclusions.

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