analyze_memory

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

No annotations are present, so the description bears full responsibility. It discloses what issues are detected and that recommendations are provided, but omits critical behavioral traits such as whether the analysis is read-only, any permission requirements, or the underlying data source (e.g., INFO). This leaves uncertainty for the agent.

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?

Two concise sentences capture the purpose and expected outcomes without redundancy. The first sentence is action-oriented, and the second enumerates detection targets, making every word valuable.

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 parameterless tool with no output schema, the description adequately covers functionality and output (recommendations). It lacks details on execution context or return format, but this is sufficient for a diagnostic tool in a family of similar analyzers.

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 zero properties, so there are no parameters to explain. The 0-parameter baseline is 4, and the description appropriately focuses on the analysis scope rather than parameter details.

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 analyzes Redis memory usage, listing specific issues it detects (fragmentation, RSS overhead, maxmemory pressure, eviction, swap risk). This distinguishes it from sibling analyze_* tools that target other subsystems like slowlog or clients.

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 use when memory-related diagnostics are needed but does not explicitly contrast with sibling alternatives. Given the sibling names (analyze_slowlog, analyze_clients, etc.), the context is clear, but no exclusion or 'when-not-to-use' guidance is provided.

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