Microsoft SQL Server MCP Server (MSSQL)

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions analyzing 'data distribution patterns' but fails to describe key behaviors: what the output looks like (e.g., statistical summaries, visualizations), whether it performs read-only operations (implied but not stated), performance implications, or any limitations like data size constraints. This is inadequate for a tool with 6 parameters and no output schema.

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's front-loaded with the core action ('Get data distribution patterns'), making it easy to parse. However, it could be slightly more structured by explicitly mentioning the target (e.g., SQL databases) to enhance clarity.

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 (6 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output format, and usage context. While the schema covers parameters, the description fails to compensate for missing annotations and output schema, leaving gaps in understanding how the tool behaves and what results to expect.

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%, so the input schema fully documents all 6 parameters with descriptions. The tool description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain how 'sampleSize' affects analysis quality). According to scoring rules, this results in a baseline score of 3, as the 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: 'Get data distribution patterns for columns to understand data quality and patterns.' It specifies the verb ('Get') and resource ('data distribution patterns for columns'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'analyze_table_stats' or 'sample_data', which might have overlapping analysis functions.

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 sibling tools like 'analyze_table_stats' or 'sample_data' that might serve similar purposes, nor does it specify prerequisites or contexts for use. This leaves the agent without clear direction on tool selection.

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