Microsoft SQL Server MCP Server (MSSQL)

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 'find columns with high null percentages' and 'analyze null patterns,' but doesn't specify what constitutes 'high' (though the schema covers minNullPercentage), how results are returned, whether it's read-only, performance implications, or authentication needs. For a tool with 4 parameters and no annotations, this is insufficient.

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: 'Find columns with high null percentages and analyze null patterns.' It's front-loaded with the core purpose and wastes no words. However, it could be slightly more structured by separating key actions, but overall it's concise.

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 (4 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover behavioral aspects like read-only nature, output format, or error handling. For a database analysis tool with siblings, more context is needed to help the agent understand when and how 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 schema fully documents all 4 parameters. The description adds no parameter-specific semantics beyond implying null percentage analysis. It doesn't explain interactions between parameters (e.g., connectionString vs. connectionName) or provide context beyond what the schema already states. Baseline 3 is appropriate when schema does the heavy lifting.

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: 'Find columns with high null percentages and analyze null patterns.' It specifies the verb ('find' and 'analyze') and resource ('columns'), but doesn't explicitly differentiate from siblings like 'analyze_data_distribution' or 'describe_table' which might also involve column analysis. The purpose is clear but lacks sibling distinction.

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. It doesn't mention prerequisites, context (e.g., data quality assessment), or exclusions. With many sibling tools for database analysis, the lack of usage guidelines leaves the agent guessing about appropriate scenarios.

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