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

There are no annotations, so the description carries the full burden. It only states the purpose and parameter guidance, but does not disclose whether this is a read-only operation, any permission requirements, error behavior, or what happens if the table doesn't exist. Though 'describe' implies a safe read, that is not explicit.

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

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

The description is efficient: a one-sentence purpose followed by a structured Args list. It is front-loaded and easy to scan. However, the Args section duplicates the schema's parameter descriptions, which is somewhat redundant but not overly verbose.

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

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

The tool is relatively simple, and the output schema is present, so return values are covered. Parameters are well-documented in both schema and description. The main gap is missing usage guidance and behavioral details, which makes it only minimally complete for such a tool.

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

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

Schema description coverage is 100%, so the baseline is 3. The description largely repeats the schema's parameter docs, with the same wording for 'table' and similar descriptions for 'catalog' and 'schema'. The only slight addition is the note 'optional if default is configured', which adds minor nuance but also potential ambiguity.

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 what the tool does: 'Describe the structure of a table (columns, types, etc).' The verb and resource are specific. It does not explicitly distinguish from sibling tools like show_create_table or get_table_stats, but the purpose is 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 through its purpose and parameter details, but it does not explicitly state when to use this tool compared to alternatives such as show_create_table or list_tables. No exclusions or alternative recommendations are 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?

With no annotations, the description carries the full burden. It discloses that write operations are disabled by default and require ALLOW_WRITE_QUERIES=true, and explains the output_file behavior (results go to disk, only confirmation returned, prevents hallucination). These are substantive behavioral details beyond the schema.

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 with a clear opening sentence, paragraphs on key behaviors, and a bulleted args list. Every sentence provides useful information with no fluff, and the most important details are front-loaded.

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 (write support, output_file, security flag), the description covers all major aspects: the query execution capability, the write enable flag, the output_file behavior and format, and the rationale for disk writing. With an output schema present, return values need not be explained. It is complete for successful invocation.

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 coverage is 100%, so baseline is 3. The description adds meaningful context beyond the schema: for output_file, it explains the rationale (prevents hallucination, enables downstream processing) and the format derivation. The query param is minimally restated, but the added value for output_file justifies a 4.

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 starts with 'Execute a SQL query and return the results' – a specific verb and resource. It clearly distinguishes itself from siblings, especially execute_query_read_only, by stating it can execute write operations (INSERT, UPDATE, DELETE) and by describing the output_file behavior for disk writes.

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 clear context for when to use this tool: for any SQL query, including write operations, and for scenarios where results should be written to disk to avoid hallucination. It does not explicitly name alternatives like execute_query_read_only or state when NOT to use this tool, so it lacks explicit when-not guidance.

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

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

With no annotations provided, the description takes on full responsibility for behavioral disclosure. It reveals key traits: validation of read-only status, optional output_file writes directly to disk, only a confirmation message returned, and format derived from file extension. This goes well beyond a simple 'runs a query' statement, though it stops short of explaining error handling or permissions.

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 a clear purpose statement, followed by concise usage notes and parameter details. Each paragraph serves a distinct role—overview, file behavior, args—and the wording is efficient. The only slight redundancy is the repeated 'not returned to the AI,' but it appears in different contexts and reinforces a critical caveat.

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 presence of an output schema, return values are covered. The description fully equips an agent to select and invoke the tool: it defines read-only scope, explains the output_file option and its format, and clarifies when results are returned vs. written to disk. No critical information is missing for safe and correct usage.

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 coverage is 100%, so baseline is 3. The description adds meaningful context for output_file—that results are written to disk and NOT returned to the AI, preventing hallucination, and explains the format derivation (.csv vs .json). This supplements the schema's own description with rationale and practical guidance, earning above baseline.

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

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

The description opens with 'Execute a read-only SQL query and return the results'—a specific verb and resource that clearly identifies the tool's function. It further distinguishes itself from the sibling execute_query by explicitly focusing on read-only queries and validation.

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 states this is 'designed for read-only queries (SELECT, SHOW, DESCRIBE, EXPLAIN, etc.)' and that it 'validates that the query is read-only before execution,' giving clear context on when to use it. It does not explicitly mention alternatives or exclusions, but the read-only scoping is evident and the sibling naming supports differentiation.

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 only restates parameter usage and does not disclose what statistics are returned, potential performance costs, permission requirements, or any other behavioral traits. The description adds no transparency beyond the schema.

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 short and front-loaded with the main purpose, followed by a structured parameter list. However, the parameter descriptions largely duplicate the schema, so some content is redundant and not earning its place.

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

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

For a simple stats retrieval tool with an output schema, the description is minimally adequate. It covers the required parameters and basic usage, but lacks guidance on when to use this tool vs alternatives and any transparency about behavior. Given the tool's simplicity, this is a moderate but acceptable level of 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 100%, so the baseline is 3. The description essentially repeats the schema descriptions without adding new meaning. It includes some guidance about fully qualified names and separate catalog/schema, but this is already present in the schema parameter description.

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 retrieves statistics for a table, naming the specific verb and resource. However, it does not explicitly differentiate itself from sibling tools like describe_table or show_create_table, though 'statistics' implies a distinct purpose.

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 guidance on how to pass parameters (prefer separate catalog/schema, fully qualified names accepted), which is a form of usage context. However, it does not explicitly state when to use this tool over alternatives like describe_table or when not to use it.

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

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

With no annotations, the description carries the full burden. 'List all available' clearly indicates a read-only enumeration operation with no parameters and no side effects, but it doesn't mention potential limitations like connectivity requirements or the exact return format (which is covered by the output schema).

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

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

The description is a single, clear sentence with no wasted words. It front-loads the action and resource.

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 parameterless listing tool with an output schema and clear sibling context, the description is complete. It accurately conveys the tool's function without needing additional details.

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, so there is nothing to explain. The description's mention of 'all available' appropriately communicates the lack of filtering, making the 4 baseline appropriate.

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

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

The description uses the specific verb 'List' and the resource 'Trino catalogs', clearly distinguishing it from sibling tools like list_schemas and list_tables. It precisely states the scope as 'all available'.

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 is straightforward but provides no explicit guidance on when to use this tool relative to siblings. The context of a hierarchy (catalogs → schemas → tables) implies it should be used first, but this is not stated.

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 does not disclose behavioral traits such as read-only nature, required permissions, or side effects. The verb 'list' implies read-only, but this is not explicitly stated.

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 very concise with a clear opening sentence and a single parameter definition. There is no fluff, and every sentence serves a purpose.

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

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

Given the tool's simplicity, one parameter, and existing output schema, the description is largely complete. It clearly states what the tool does and accepts, though it omits usage guidance and behavioral details.

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% for the catalog parameter. The description adds slight context by saying 'to list schemas from', but mostly repeats schema information. Therefore, it meets the baseline for high 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 'List all schemas in a catalog.' with a specific verb and resource. This distinguishes it from sibling tools like list_catalogs and list_tables, which target different objects.

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

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

The description does not explicitly mention when to use this tool versus alternatives. Usage is implied from the function statement, but there is no exclusion or comparison with list_catalogs or list_tables.

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

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

With no annotations provided, the description carries the full burden of disclosing behavior. It only says 'List all tables' without noting that it is a read-only operation, whether it returns table names only, or if any permissions are required. This is a significant gap for a tool that could be part of a database management 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 extremely concise, with the main action in the first sentence and a compact args list. Every sentence serves a clear purpose, and there is no fluff or unnecessary detail, making it highly scannable and effective.

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

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

The tool is simple and the description covers the core function, but it lacks context about return values (though an output schema exists) and does not mention any behavioral nuances or prerequisites. Given the presence of an output schema, the lack of return details is partially compensated, but the description still feels minimal for a tool with no annotations.

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 coverage is 100%, with both parameters already described in the schema ('The schema name' and 'The catalog name'). The description repeats this information in an 'Args' section without adding any extra meaning, such as formatting, constraints, or default behavior. Baseline 3 is appropriate when the schema fully documents parameters.

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 'List all tables in a schema' with a specific verb and resource, and the title 'list_tables' reinforces this. It distinguishes itself from sibling tools like list_catalogs, list_schemas, and describe_table by focusing on tables within a given catalog and schema.

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

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

The description implies usage when you need to list tables, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. Sibling tools like list_catalogs and list_schemas are not mentioned, so guidance is only implicit, not explicit.

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

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

With no annotations, the description carries the full burden. It clearly conveys a read-only operation ('Show') and the parameter default behavior for catalog/schema. While it does not explicitly state 'read-only' or error conditions, the behavior is transparent enough for an agent to understand the primary outcome.

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 brief and front-loads the purpose. The Args section is somewhat redundant with the schema, but the overall length is reasonable and does not waste words.

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 tool with an output schema, the description sufficiently covers the main function and parameter usage. It does not detail return format, but the output schema handles that. The description is complete enough for an agent to select and invoke the tool correctly.

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 for all three parameters, and the description's Args section largely duplicates the schema's descriptions. The description adds no new semantics beyond what the schema already provides, so the baseline score of 3 applies.

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

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

The description clearly states the tool's purpose with a specific verb and resource: 'Show the CREATE TABLE statement for a table.' This distinguishes it from sibling tools like describe_table, which focuses on table metadata rather than DDL.

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 when to use the tool (when a CREATE TABLE statement is needed) and provides guidance on parameter formatting (preferring separate catalog/schema parameters). However, it does not explicitly mention alternatives or when not to use it, leaving some ambiguity.

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