analyze_workload_indexes

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 mentions analysis and recommendation but does not cover critical aspects like whether this is a read-only operation, if it requires specific permissions, potential performance impact, or output format. For a tool that likely involves database analysis, this omission is significant and limits the agent's understanding of its behavior.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and avoids redundancy, making it easy for an agent to parse quickly. Every part of the sentence contributes to understanding the tool's function.

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 complexity of database analysis tools and the lack of annotations and output schema, the description is insufficient. It does not explain what the recommendations look like, how they are generated, or any behavioral traits like safety or side effects. With siblings that may overlap, more context is needed to ensure the agent can use this tool effectively in the broader toolset.

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, documenting both parameters ('max_index_size_mb' and 'method') with details like defaults and enums. The description does not add any semantic information beyond the schema, such as explaining when to choose 'dta' vs 'llm' methods or the implications of index size limits. Thus, it meets the baseline but does not enhance parameter understanding.

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 frequently executed queries in the database and recommend optimal indexes.' This specifies the verb ('analyze'), resource ('frequently executed queries'), and outcome ('recommend optimal indexes'). However, it does not explicitly differentiate from sibling tools like 'analyze_query_indexes' or 'get_top_queries', which may have overlapping functionality, so it falls short of a perfect score.

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 no guidance on when to use this tool versus alternatives. With siblings such as 'analyze_query_indexes', 'get_top_queries', and 'explain_query', there is no indication of context, prerequisites, or exclusions. This lack of differentiation leaves the agent to infer usage, which is inadequate for effective tool selection.

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