MSSQL MCP Server

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 hints at a default threshold), how results are returned, whether it's read-only or has side effects, or any performance considerations. For a tool with 4 parameters and no annotations, this leaves significant behavioral gaps.

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 extremely concise and front-loaded: 'Find columns with high null percentages and analyze null patterns.' It uses a single, efficient sentence that directly states the tool's function without any redundant information, making it easy to parse and understand quickly.

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 lacks details on behavioral traits, output format, error handling, or how it differs from sibling tools. While the schema covers parameters well, the description doesn't compensate for missing annotations or output information, leaving gaps in understanding the tool's full context.

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 schema description coverage is 100%, with all parameters well-documented in the input schema (e.g., connectionString, minNullPercentage). The description adds no additional parameter semantics beyond what's in the schema, such as explaining interactions between parameters or usage nuances. According to the rules, with high schema coverage, the baseline is 3, which is appropriate here.

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'), making the intent unambiguous. However, it doesn't explicitly differentiate from siblings like 'analyze_data_distribution' or 'analyze_table_stats,' which might also involve data quality analysis, leaving some ambiguity about its unique role.

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 many sibling tools focused on database analysis (e.g., 'analyze_data_distribution,' 'analyze_table_stats'), there's no indication of specific scenarios, prerequisites, or exclusions. This lack of context could lead to confusion about selecting the right tool for null-related tasks.

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