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

Annotations cover readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds behavioral value by specifying exactly what is returned (full schema details plus up to 3 sample rows) and the purpose ('data format is visible'). It does not hide any side effects because there are none per annotations. Slight deduction for not mentioning any output limitations (e.g., max rows is already stated), but overall it adds meaningful behavioral context beyond 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 compact, uses clear formatting, and every sentence contributes value. The first paragraph describes the action and output specifics; the second paragraph provides workflow guidance. No filler 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?

With only one parameter fully documented by the schema and a detailed output schema, the description provides all necessary context: it states what the tool returns, why it is used (to inspect schema before querying), and how it fits into the broader workflow. Nothing an agent needs to decide when and how to invoke it is missing.

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 already provides 100% parameter description: the only parameter 'table_name' includes a clear description and example ('orders'). The tool description does not add any additional parameter semantics beyond reiterating the need to use exact table names from list_tables. With full schema coverage, the baseline 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 action ('show the full schema of one table') and specifies the exact details returned: columns with types, NOT NULL and primary-key flags, foreign keys, and up to 3 sample rows. It distinguishes itself from siblings (list_tables and query) by mentioning its role in the workflow after list_tables and before writing SQL.

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 explicitly tells when to use the tool: 'Use after list_tables to learn exact column names and types before writing SQL for the query tool.' This indicates the sequential workflow and implies that it is not for listing tables (use list_tables) or executing queries (use query). The usage context is unambiguous.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds concrete behavioral context beyond that: it specifies that the tool returns row counts, column names, descriptions, and the relationships between the four specific tables. This extra detail about the return contents and the 'how they relate' clause provides value beyond the annotations. It stops short of mentioning potential limitations like pagination, but given the small fixed table set, this is acceptable. No contradiction with 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 three sentences, all of high value. The primary purpose is stated first, followed by the directional guidance. No redundant wording or filler. Every sentence earns its place, making it concise 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?

For a no-parameter discovery tool, the description covers everything an agent needs: what it does, what it returns, and how it relates to siblings. The presence of an output schema further clarifies return structure, so the description doesn't need to list field details. The tool is simple, and the description is fully complete on its own.

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 zero parameters, so there is nothing to explain. The description has no parameter information, but none is needed. Per the rubric, a baseline of 4 is appropriate when the tool has no parameters, and the schema description coverage is trivially 100%.

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 states a specific verb and resource ('List every table in the shop database') and lists the exact data returned (row count, column names, descriptions). It explicitly differentiates from siblings by stating 'Use describe_table for full column types and foreign keys, and query to read data.' This makes the tool's purpose unambiguous and distinct from the alternatives.

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 explicitly instructs 'Call this first to discover the database structure,' providing a clear when-to-use directive. It also names the alternatives for more detailed needs (describe_table for schema details, query for data reads), giving explicit routing guidance without ambiguity.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds value beyond annotations by disclosing the exact return shape (columns, rows, row_count, truncated, next_offset), truncation behavior, and pagination. It does not contradict the 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 a single well-organized paragraph that front-loads the purpose before the return format and usage guidance. Every sentence earns its place — no filler or repetition of the title. Slightly longer than strictly necessary but efficiently 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?

For a SQL-execution tool of moderate complexity with rich annotations and full schema coverage, the description is nearly complete. It explains return values and pagination even though an output schema exists (somewhat redundant per rubric, but reinforced here). Nothing critical is missing for correct 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 description coverage is 100%, so the schema fully documents sql, limit, and offset. The description adds marginal value by reinforcing preference for SQL aggregation over fetching many raw rows and mentioning next_offset for pagination, but the baseline 3 is appropriate since the schema carries the parameter documentation burden.

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 states a specific verb ('Execute') and resource ('read-only SQL query against the shop database'), with the intent to return matching rows as JSON objects. It clearly distinguishes this from the sibling tools list_tables and describe_table, which are about schema discovery rather than query execution.

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 gives explicit when-to-use guidance: use list_tables/describe_table first to learn the schema, and tells the agent to aggregate in SQL (SUM, COUNT, GROUP BY, ORDER BY, LIMIT) for totals, top-N, or revenue questions instead of fetching raw rows. This directly routes the agent to the right tool and strategy.

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