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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: data source (Dune Analytics query), transformation (pivoted to show unique active wallets per day by platform), output format (markdown table or error message), and a default parameter value. However, it lacks details on rate limits, authentication needs, or query failure specifics.

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, followed by concise sections for arguments and returns. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.

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 (data fetching and transformation), no annotations, and no output schema, the description is largely complete. It covers purpose, data source, parameter semantics, and output format. However, it could improve by mentioning potential limitations or error conditions more explicitly.

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 meaning by explaining that 'limit' controls the maximum rows fetched from the Dune query, with a default of 1000, which clarifies its purpose beyond the schema's basic type and title. Since there is only one parameter, this is sufficient for good 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 ('fetch the daily count'), resource ('active wallet addresses'), and scope ('interacting with Solana memecoin launchpads'). It distinguishes from siblings by focusing on active addresses rather than graduates, graduation rates, or tokens deployed, making the purpose unambiguous.

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

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

The description implies usage for retrieving daily active wallet data from Dune Analytics, but it does not explicitly state when to use this tool versus the sibling tools (e.g., for analyzing user engagement vs. token metrics). No exclusions or alternatives are provided, leaving some ambiguity in 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?

With no annotations provided, the description carries the full burden and does well by disclosing key behaviors: it retrieves data from Dune Analytics, pivots it, and returns a markdown table or error message. It mentions the query might fail, adding context. However, it lacks details on rate limits, authentication needs, or data freshness, which could be important for an agent.

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: the first sentence states the purpose clearly, followed by additional context in a second sentence, and then structured sections for Args and Returns. Every sentence adds value without redundancy, 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?

Given the tool's moderate complexity (fetching and processing data from an external source), no annotations, and no output schema, the description is fairly complete. It explains what the tool does, the parameter, and the return format. However, it could improve by detailing error conditions or data sources more explicitly, but it covers the essentials well.

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 adds meaning by explaining the 'limit' parameter as 'Maximum number of rows to fetch from the Dune query' with a default of 1000, which clarifies its purpose beyond the schema's basic type and title. Since there is only one parameter, this is sufficient for good 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 verb 'fetch' and the resource 'daily number of graduates from Solana memecoin launchpads', specifying it retrieves data from Dune Analytics and pivots it to show graduates per day by platform. It distinguishes from siblings like 'get_daily_active_addresses' or 'get_daily_tokens_deployed' by focusing on graduates rather than addresses or tokens.

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 fetching graduate data from launchpads, but does not explicitly state when to use this tool versus alternatives like 'get_daily_graduation_rate' (which might provide rates rather than counts). No exclusions or specific contexts are provided, leaving some ambiguity in tool selection.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the data source (Dune Analytics query), transformation (pivoting to show graduation rate per day by platform), and output format (markdown table or error message), which covers key behavioral aspects. However, it doesn't mention rate limits, authentication needs, or error handling specifics beyond a generic mention of query failure.

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 well-structured and concise, with a clear purpose statement followed by specific details about data source, transformation, parameters, and return values. Every sentence adds value without redundancy, and it's appropriately front-loaded with the core functionality.

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 (one optional parameter, no output schema, no annotations), the description provides good completeness. It explains what the tool does, the parameter's purpose, and the return format. However, it could be more complete by addressing potential limitations or edge cases, such as what happens if the Dune query times out or how historical data availability is handled.

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

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

The description adds meaningful context for the single parameter 'limit', explaining it controls 'maximum number of rows to fetch from the Dune query' with a default of 1000. Since schema description coverage is 0% (the schema only lists 'limit' as an integer with default 1000 without semantic explanation), the description compensates well by providing clear parameter semantics 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', 'retrieves') and resources ('daily graduation rate of Solana memecoin launchpads'), distinguishing it from sibling tools like get_daily_active_addresses or get_daily_tokens_deployed by focusing on graduation rates rather than addresses or token deployments.

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 retrieving graduation rate data from Dune Analytics, but does not explicitly state when to use this tool versus alternatives or provide context on prerequisites. It mentions the data source and transformation, which gives some implied guidance, but lacks explicit when/when-not instructions.

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 and does well by disclosing: data source (Dune Analytics query), transformation (pivoting to show per-platform counts), optional percentage calculation, error handling (returns error message on query failure), and output format (markdown table). It doesn't mention rate limits, authentication needs, or data freshness.

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?

Perfectly structured with purpose statement, implementation details, parameter explanations, and return format - all in 4 focused sentences. Every sentence earns its place with no redundancy or fluff.

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 provides excellent coverage of purpose, parameters, and return format. It could improve by mentioning data freshness, query execution time, or rate limits, but covers the essentials well given the 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?

With 0% schema description coverage, the description fully compensates by explaining both parameters: 'return_percent' controls percentage vs. absolute value formatting with clear default, and 'limit' specifies maximum rows from the query with default value. The description adds crucial meaning 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 specific action ('retrieve the daily count'), resource ('tokens deployed by Solana memecoin launchpads'), and data source ('fetches data from a Dune Analytics query'). It distinguishes from siblings by focusing on token deployments rather than addresses, graduates, or graduation rates.

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 analyzing token deployment trends and offers an optional percentage format, but doesn't explicitly state when to use this tool versus alternatives or provide context about prerequisites. No sibling tool comparisons or exclusion criteria are mentioned.

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