MCP Vertica

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. While it mentions that results are returned as a string, it doesn't cover critical aspects like whether the query is read-only or can modify data, authentication requirements, error handling, performance implications, or rate limits. For a SQL execution tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 appropriately sized, with clear sections for Args and Returns. Each sentence adds value without redundancy. However, the inclusion of 'ctx' in the Args section without explanation slightly reduces efficiency, as it doesn't clarify its purpose to the agent.

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 executing SQL queries, the lack of annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It doesn't address safety concerns (e.g., read vs. write operations), result formatting beyond 'string', or error scenarios. For a tool that could potentially modify data or return complex results, this leaves too many unknowns for reliable agent 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 description lists three parameters (ctx, query, database), but the input schema only documents one (query) with 0% schema description coverage. The description adds some semantic context by explaining that 'query' is a SQL query and 'database' is optional, but it doesn't specify format, constraints, or what 'ctx' entails. Since schema coverage is low, the description partially compensates but doesn't fully bridge the gap.

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: 'Execute a SQL query and return the results.' It specifies the verb (execute) and resource (SQL query), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like stream_query or get_table_structure, which prevents a perfect score.

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 siblings like stream_query, list_views, and get_table_structure available, there's no indication of when execute_query is appropriate versus when other tools might be better suited. The absence of usage context leaves the agent without decision-making criteria.

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