PostgreSQL-Performance-Tuner-Mcp

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

Annotations already indicate read-only, non-destructive, and idempotent behavior, which the description does not contradict. The description adds valuable context by specifying the exclusion of PostgreSQL system tables from analysis and detailing the aspects analyzed (e.g., lock contention, replication status), which goes beyond the annotations to clarify scope and focus.

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, starting with the core purpose, followed by a note on scope, a bulleted list of analyzed aspects, and a summary of the output. Each 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 complexity of a health check tool with no output schema, the description provides a thorough overview of what is analyzed and the output format (health score with breakdown and recommendations). It compensates well for the lack of output schema, though it could benefit from more explicit usage guidelines relative to siblings.

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 clear documentation for both parameters ('include_recommendations' and 'verbose'). The description mentions that the tool 'returns a health score with detailed breakdown and recommendations,' which aligns with the parameters but does not add significant meaning beyond what the schema already provides, meeting 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 clearly states the tool performs a 'comprehensive database health check' on PostgreSQL, specifying it analyzes user/client tables while excluding system tables. It distinguishes itself from sibling tools like 'get_table_stats' or 'analyze_index_bloat' by covering multiple aspects (e.g., connection statistics, cache ratios, replication) rather than focusing on a single metric.

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 for overall database monitoring and health assessment, with the note about excluding system tables providing some context. However, it does not explicitly state when to use this tool versus alternatives like 'get_bloat_summary' or 'review_settings', nor does it mention prerequisites or exclusions beyond the table scope.

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