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

The annotations already indicate destructiveHint=true, so the destructive nature is clear. The description adds behavioral info: single statements only, cross-database support, and avoidance of USE statements. This adds value beyond 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 two sentences long, well-structured, and front-loaded with the core action. Every sentence adds value without redundancy. No 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?

Given the tool's complexity (SQL execution), the description covers usage guidelines and parameter semantics well. However, it lacks any mention of output format (e.g., rows for SELECT, affected rows for DML) or error handling, which would be helpful since no output schema exists.

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 the schema already documents the 'query' parameter. The description adds practical guidance like using fully qualified names and avoiding USE statements, which enriches understanding beyond the schema's basic 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 it executes SQL statements against MySQL server and lists supported statement types (SELECT, DML, SHOW, DESCRIBE, ad-hoc). It distinguishes from USE statements and mentions cross-database queries. However, it doesn't explicitly differentiate from sibling tools like get_schema_info, so a 4 is appropriate.

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 states when to use the tool: for SELECT, DML, SHOW, DESCRIBE, and ad-hoc queries. It also provides guidance to use fully qualified names instead of USE statements and to use single statements only. This gives clear context for appropriate usage, though it doesn't mention when not to use it (e.g., for metadata queries).

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 and destructiveHint=false, so the safety profile is covered. The description adds that it returns specific metadata and uses MYSQL_DATABASE, which is useful but not extensive.

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?

Three sentences: first states purpose, second gives usage advice, third explains parameter usage. Front-loaded and no superfluous 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 read-only metadata tool with one optional parameter, the description covers what it returns and how to use it. Output schema is absent, but the description lists the metadata fields, which is sufficient.

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 a description for the parameter. The description adds valuable context: omitting table_name returns all tables, and bare names use MYSQL_DATABASE. This goes beyond the schema 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's purpose: 'Get column metadata for a table or all tables in the configured database' with a specific list of metadata included (column names, data types, etc.). It distinguishes from siblings by implying it's for schema exploration before querying.

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?

Explicitly advises 'Call this before querying an unfamiliar table' and explains optional usage with 'Omit table_name to see all tables at once.' Lacks direct comparison with sibling tools but provides clear context for when to use.

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 mark the tool as readOnlyHint=true and destructiveHint=false. The description adds transparency by specifying 'small sample' and the default/max limit behavior, which is valuable beyond annotations. No contradictions detected.

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 sentences, no redundant words. The first sentence front-loads the core purpose; the second adds usage and naming tips. Every sentence earns 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 tool with two parameters and no output schema, the description covers the essential aspects: what it does, how to use it, and naming conventions. It is complete enough for an AI agent to select and invoke 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% (both parameters described). The description adds value by explaining that table_name can be bare (using MYSQL_DATABASE) or in database.table format, which goes beyond the schema's description. For limit, the schema already states default and max, so no further addition needed.

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 ('Fetch a small sample'), the resource ('from a table'), and the purpose ('to understand its data format and content'). It distinguishes itself from sibling tools by mentioning alongside get_schema_info and before writing complex queries, implying this tool is for exploration, not execution or schema understanding.

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 advises using the tool alongside get_schema_info before writing complex queries, providing clear context for when to use it. It also explains naming conventions (bare table vs database.table). However, it lacks explicit guidance on when not to use it or comparison to execute_sql for arbitrary queries.

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