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

No annotations are provided, so the description carries the full behavioral burden. The verb '获取' strongly implies a read-only retrieval, and the description states what is returned (columns, indexes, DDL). However, it does not explicitly disclose that no data is modified, nor does it mention prerequisites like table existence or required permissions. It is adequate but not rich in behavioral detail.

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 one information-dense sentence: the primary action is stated first, followed by a specific list of returned components. There is absolutely no filler or repetition, 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?

The input schema fully documents both required parametersessed an output schema exists to describe return structure. The description communicates the tool's core purpose and return contents. The main gaps are lack of explicit usage guidance and any confirmation of read-only behavior, but for a simple metadata retrieval operation these are minor.

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%; both parameters (database, table) already have clear Chinese descriptions in the input schema. The tool description adds no extra meaning or constraints beyond the schema, so 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 uses a specific verb (获取/get) and resource (表的详细结构信息/detailed table structure), then enumerates the exact content returned: column definitions, indexes, and DDL. This clearly distinguishes it from siblings like query (row data), get_table_sample (sample rows), and get_table_stats (statistics).

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 schema details are needed, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. It names no sibling tools and gives no 'when not to use' guidance, so the usage context is only implied through the description's wording.

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 behavioral burden, and it does disclose the core mutation side effect and the read-only gate. It flags a critical configuration-dependent behavior that an agent must know before calling. It does not detail transaction/commit behavior or destructive-statement impact, which would push it to a 5.

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 two short sentences with no filler. The key warning about read-only mode is front-loaded after the core purpose. Every word contributes to either defining the operation or preventing misuse.

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 description covers the purpose, the acceptable SQL statement types, and the critical gating condition, while the output schema handles return values. The main gaps are transactionality and the consequences of destructive statements, but these are secondary given the clear mutation warning. Overall, an agent has enough 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?

Schema description coverage is 100%, and both parameters already carry descriptive text, so the description adds little beyond the structured schema. The mention of INSERT/UPDATE/DELETE reinforces what the sql parameter accepts, but that same constraint is already in the parameter description. Baseline 3 is appropriate given the full 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 explicitly states the tool executes write operations (INSERT/UPDATE/DELETE), which is a specific verb-resource pairing. This clearly distinguishes it from the read-only siblings such as query and list_tables. The tool is unambiguously the write counterpart in the set.

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 a clear precondition: the tool is only available when read-only mode is disabled, with the exact environment variable named. It does not explicitly say 'use query for SELECT', but the restriction to INSERT/UPDATE/DELETE makes the usage boundary clear. This is solid context without an explicit alternatives section.

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 burden. '获取执行计划' conveys a read-only diagnostic action, but it does not explicitly state whether the query is executed, whether there are side effects, or any cost/performance implications beyond the purpose.

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?

One concise sentence in Chinese that front-loads the operation and purpose. There is no filler or redundant restatement of the tool name/title.

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 two-parameter tool with a documented output schema, the description is largely sufficient: it states what the tool does and when it is useful. It could add a note about not modifying data, but the read-only nature is strongly implied by '获取执行计划'.

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 documents the sql and database parameters. The description does not add parameter-level semantics beyond the purpose, matching 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 uses a specific verb ('获取') and resource ('SQL查询的执行计划'), and adds the purpose '用于分析查询性能'. This clearly identifies the tool as an explain/plan analysis utility rather than a data-returning query tool, though it does not explicitly name sibling tools.

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?

It explicitly states the context: analyzing query performance. It does not provide exclusions or compare itself to alternatives like query or execute_write, so it misses the explicit when/when-not guidance, but the stated purpose gives clear 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 present, so the description carries the full burden. It discloses the default 5-row limit but does not address ordering guarantees, read-only nature explicitly, or behavior beyond the limit. For a sampling tool, this is minimal but not misleading.

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 one concise Chinese sentence, front-loads the action, and includes the default behavior. There is no wasted wording 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 an output schema and 100% parameter coverage, the description is complete for a simple sample tool. The only notable gap is the absence of explicit guidance on ordering or when to prefer this over query, but this is minor given the tool's simplicity.

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%, with all three parameters (database, table, limit) already described. The description only repeats the default limit value and adds no extra parameter-level meaning beyond the 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 '获取表的样本数据' (get sample data of a table) with a specific default of '前5行' (first 5 rows). This distinguishes it from siblings like query (custom queries) and describe_table (schema information).

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 phrase '快速了解数据内容' implies a quick-preview use case, but it does not explicitly state when to use this tool over query or describe_table, nor does it mention exclusions or alternatives.

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 must carry the full behavioral disclosure burden. The verb '获取' indicates a non-mutating fetch, and the listed metrics clarify what the operation produces, but there is no mention of cost, freshness, approximation, 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 a single, efficiently written sentence that front-loads the core action and follows it with a clear comma-separated list of output categories. There is no filler or redundant wording.

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-style tool with two required parameters and an output schema, the description is essentially complete for invoking the tool correctly. The only meaningful gap is the absence of explicit guidance for choosing this tool over closely related sibling tools.

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%, with 'database' and 'table' already documented as 数据库名称 and 表名称. The tool description adds no parameter-level detail beyond what the schema provides, so it meets the baseline but does not exceed it.

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 a specific verb ('获取') and resource ('表的统计信息'), and enumerates concrete output types (行数, 数据大小, 索引大小, 列基数). This is clear and helps distinguish it from generic query/describe siblings, though it does not explicitly name or contrast any sibling.

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 the tool is for retrieving table statistics and therefore carries contextual usage guidance. However, it does not state when to prefer this over describe_table or explain_query, nor does it provide any 'when not to use' 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose that only accessible databases are returned, but it does not explicitly state that the operation is read-only, requires specific privileges, or has any other behavioral traits an agent should know.

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, front-loaded sentence. Every word contributes to the meaning, and there is no padding, repetition, or unnecessary structure.

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 zero-parameter listing tool with an output schema, the description is sufficient to convey the core behavior and scope. It does not explain return values, but the output schema covers that, so nothing critical 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 tool has zero parameters and the schema has 100% coverage, so there is no parameter ambiguity. The description correctly avoids describing nonexistent parameters, matching the baseline for tools with no 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 states a specific action (列出/list) on a specific resource (databases) with a clear scope (all accessible databases on the MySQL server). It distinguishes this tool from siblings like list_tables and describe_table by making the database-level target explicit.

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 only says what the tool does; it gives no explicit guidance on when to use it versus alternatives like list_tables or query. There are no exclusions, prerequisites, or contextual hints about when this tool is the right choice.

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?

There are no annotations, so the description carries full behavioral burden. It clearly states the result is all tables in the specified database, but it does not disclose whether views/system tables are included, ordering, error behavior, or permission requirements. This is minimal but acceptable for a simple read-only enumeration.

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, front-loaded sentence with no filler. It is appropriately sized for a one-parameter list operation and every word contributes meaning.

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 one required parameter is fully documented and an output schema exists, so return structure does not need to be explained in the description. The only gap is lack of scoping detail about what counts as 'all tables', but for a simple enumeration tool this is mostly complete.

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 documents the single database parameter with 100% coverage as '数据库名称', so the description adds no new semantic detail. The description only restates that the target is the specified database, which matches the baseline expectation when schema coverage is high.

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 the specific operation (列出/list) applied to a clear resource (所有表/all tables in a specified database). This distinguishes it from sibling list_databases, which lists databases, and describe_table, which targets a single table.

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 explicit when-to-use guidance or alternatives are named. The wording implies it should be chosen when enumerating tables within a specific database rather than listing databases or inspecting a single table, but the agent is left to infer this from the sibling tool names.

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 behavioral burden. It clearly discloses the read-only safety property and the allowed SQL statement set, which is the most important behavioral context. It does not detail result limits or error handling, but the output schema covers return-value expectations.

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 sentence that names the action, the read-only constraint, and the supported SQL statements. Every element carries signal and there is no repetition of the tool name or schema.

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 read-only SQL query tool with a complete input schema and an output schema, the description plus schema is sufficient to call the tool correctly. A small gap is that it does not clarify when to prefer specialized siblings like describe_table or explain_query, but this does not block 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%: sql and database already have meaningful descriptions. The tool description adds no parameter-level detail, so the baseline 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 states a clear verb and resource: execute SQL queries, and further restricts the scope to read-only statements (SELECT/SHOW/DESCRIBE/EXPLAIN). This also separates it from sibling execute_write, whose purpose is write operations.

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?

It explicitly states when the tool is appropriate: any read-only SQL query among the listed statement types, and explicitly excludes write statements via '只读' and '仅支持'. However, it does not name the alternative tool (execute_write) for unsupported write operations, though that is inferable from the sibling list.

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