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 generating a report with 'historical data, current price, and stability analysis,' which gives some context on output content, but lacks details on behavioral traits such as data sources, rate limits, error handling, or whether it performs computations or fetches external data. This leaves gaps for an AI agent to understand operational aspects.

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 appropriately sized and front-loaded: it starts with the core purpose, followed by structured sections for 'Args' and 'Returns.' Each sentence adds value without redundancy, making it easy to scan and understand quickly. No wasted words or unnecessary details are present.

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 (analysis report generation) and lack of annotations and output schema, the description is moderately complete. It covers the purpose, parameters, and return format (Markdown report), but lacks details on behavioral aspects like data freshness, accuracy, or potential limitations. For a tool with no structured output schema, more context on report structure or analysis methods would enhance completeness.

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

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

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics beyond the schema by explaining 'coin' as 'The symbol of the stablecoin (e.g., 'usdt', 'usdc', 'dai')' and 'days' as 'Number of days for analysis. Defaults to 7,' including an example and default value. This clarifies parameter usage effectively, though it could provide more on constraints or validation.

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: 'Generate a peg stability analysis report for a USD-pegged stablecoin.' It specifies the verb ('generate'), resource ('report'), and scope ('USD-pegged stablecoin'), but does not explicitly differentiate it from sibling tools like 'get_historical_data' or 'get_current_price' which might provide related data without analysis.

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

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

The description implies usage by specifying the type of analysis ('peg stability analysis report') and the target ('USD-pegged stablecoin'), suggesting it's for evaluating stablecoin performance. However, it does not provide explicit guidance on when to use this tool versus alternatives like 'get_historical_data' or 'get_current_price', 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.

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

With no annotations provided, the description carries full burden. It discloses the core behavior (fetching price and calculating deviation) and output format (Markdown string). However, it lacks details about data sources, rate limits, error conditions, or whether this is a read-only operation (though implied by 'fetch' and 'calculate').

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 efficiently structured with a clear purpose statement, followed by dedicated 'Args' and 'Returns' sections. Every sentence adds value: the first states the tool's function, while the subsequent sections document parameters and output without redundancy.

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

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

For a single-parameter tool with no annotations or output schema, the description is reasonably complete. It covers the purpose, parameter semantics, and output format. However, it could improve by mentioning data sources or error handling, given the lack of structured fields.

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 0%, so the description must compensate. It provides clear semantics for the single parameter 'coin', including its type (str), purpose (stablecoin symbol), and examples ('usdt', 'usdc', 'dai'). This fully documents the parameter beyond the bare schema.

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

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

The description clearly states the tool's purpose with specific verbs ('fetch', 'calculate') and resources ('current price of a USD-pegged stablecoin', 'peg deviation'). It distinguishes from sibling tools like 'get_historical_data' (historical vs current) and 'analyze_peg_stability' (stability analysis vs price fetching).

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 through the parameter description ('symbol of the stablecoin') and mentions peg deviation calculation, which suggests it's for monitoring stablecoin pegs. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_supported_stablecoins' (list available coins) or 'analyze_peg_stability' (deeper analysis).

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 for behavioral disclosure. It mentions the return format but doesn't describe error conditions, rate limits, authentication requirements, data freshness, or what happens with invalid inputs. The description is minimal 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.

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

The description is efficiently structured with a clear purpose statement followed by organized Args and Returns sections. Every sentence adds value with no redundant information, making it easy to parse quickly.

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

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

For a 2-parameter tool with no annotations and no output schema, the description covers basic purpose and parameters adequately but lacks behavioral context. It doesn't explain error handling, data sources, or limitations that would help an agent use it correctly in various scenarios.

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

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

With 0% schema description coverage, the description provides essential parameter context: coin is a stablecoin symbol with examples, and days is optional with default value and meaning. This compensates well for the schema gap, though it doesn't specify constraints like valid coin values or day ranges.

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 ('Fetch historical price data'), target resource ('USD-pegged stablecoin'), and output format ('Markdown table'). It distinguishes from siblings like get_current_price (current vs historical) and analyze_peg_stability (analysis vs raw data).

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 for historical price data retrieval, but doesn't explicitly state when to use this tool versus alternatives like get_current_price or analyze_peg_stability. No guidance on prerequisites, limitations, or exclusion criteria 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 burden. It discloses the return format ('Markdown-formatted table') and content ('symbols and descriptions'), which is useful behavioral context. However, it does not mention other traits like rate limits, authentication needs, or error handling, leaving gaps for a tool with no annotations.

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 front-loaded with the core purpose in the first sentence, followed by a concise return specification. Both sentences earn their place by providing essential information without redundancy, making it highly efficient and well-structured.

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 simplicity (0 parameters, no output schema, no annotations), the description is mostly complete: it states the purpose, return format, and content. However, without annotations, it could benefit from mentioning behavioral aspects like safety or performance, but for a read-only list tool, this is a minor gap.

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

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

The tool has 0 parameters, and schema description coverage is 100% (as there are no parameters to describe). The description does not need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as per the rules.

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

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

The description clearly states the verb ('Fetch') and resource ('list of supported USD-pegged stablecoins'), specifying both the scope ('USD-pegged') and the content ('symbols and descriptions'). It distinguishes from siblings like 'analyze_peg_stability' (which analyzes rather than lists) and 'get_current_price' (which fetches prices rather than metadata).

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 by stating what the tool returns (a Markdown table of stablecoin data), but it does not explicitly guide when to use this tool versus alternatives like 'get_current_price' for price data or 'analyze_peg_stability' for stability analysis. No exclusions or prerequisites are mentioned.

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