analyze_storage_engines

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

Annotations already indicate read-only, non-destructive, and idempotent behavior. The description adds valuable context beyond this: it specifies that analysis excludes MySQL system tables by default, mentions it's based on MySQLTuner's patterns, and lists the types of information provided (e.g., engine status, table counts, metrics, recommendations). This enhances understanding without contradicting 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 well-structured and front-loaded: it starts with the core purpose, lists key outputs in bullet points, adds important notes, and cites the source pattern. 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 (analysis with optional filtering), rich annotations (read-only, idempotent), and no output schema, the description is largely complete. It covers purpose, outputs, exclusions, and context. A slight gap is the lack of explicit output format details, but the bullet points provide enough semantic understanding for an agent to use it effectively.

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 input schema fully documents the two parameters (include_table_details and schema_name). The description doesn't add specific parameter details beyond what's in the schema, but it implies the scope of analysis (user tables, optional schema filtering), aligning with the schema. Baseline 3 is appropriate as the schema handles parameter documentation.

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: 'Analyze storage engine usage and statistics for user tables.' It specifies the verb ('analyze') and resource ('storage engine usage and statistics for user tables'), and distinguishes it from siblings by focusing on storage engines rather than other MySQL components like indexes, queries, or replication.

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 analyzing storage engines in MySQL, with a note that it excludes system tables by default. It doesn't explicitly state when not to use it or name alternatives among siblings, but the focus on storage engines implies it's not for other analysis types like 'analyze_query' or 'get_slow_queries'.

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