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 the tool identifies tables 'automatically based on table patterns,' which hints at heuristic analysis, but doesn't explain what constitutes a lookup table, how patterns are defined, or what the output format is. For a tool with no annotations and no output schema, this leaves significant gaps in understanding its behavior and results.

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: 'Identify reference/lookup tables automatically based on table patterns.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence contributes directly to understanding the tool's function.

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 identifying lookup tables heuristically, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of table names, patterns found), how it handles errors, or any behavioral nuances like performance implications. For a tool with 4 parameters and no structured output guidance, more context is needed to fully understand its use.

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 100% description coverage, so the schema fully documents all parameters (connectionString, connectionName, schema, maxRows). The description adds no additional parameter semantics beyond what's in the schema, such as examples of table patterns or how maxRows influences identification. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to heavily.

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: 'Identify reference/lookup tables automatically based on table patterns.' It specifies the verb ('identify'), resource ('reference/lookup tables'), and method ('based on table patterns'), which distinguishes it from generic table listing tools. However, it doesn't explicitly differentiate from all sibling tools like 'list_tables' or 'analyze_table_stats' beyond the pattern-based identification focus.

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, such as needing a database connection, or compare it to sibling tools like 'list_tables' for general listing or 'analyze_table_stats' for analysis. The context is implied through the tool name and description but lacks explicit usage instructions.

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