SD Elements MCP Server

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

With no annotations provided, the description carries full burden of behavioral disclosure. While it documents the query structure thoroughly, it doesn't describe what the tool actually does behaviorally - whether it returns data immediately, triggers async processing, has rate limits, requires specific permissions, or what format the results take. The focus is on input format rather than tool 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 appropriately sized and front-loaded with the core purpose, followed by detailed parameter documentation. Every sentence serves a purpose - purpose statement, parameter format note, structure documentation, and example. It could be slightly more concise in the structure listing but remains well-organized.

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?

For a complex analytics tool with no annotations and no output schema, the description provides excellent input documentation but lacks critical behavioral and output information. The query structure is thoroughly documented, but users don't know what format results take, whether queries are cached, what errors might occur, or performance characteristics. The input focus is strong but overall context is incomplete.

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 adds substantial value beyond the 100% schema coverage. While the schema only documents 'query' as a JSON string or object, the description provides complete query structure documentation with required/optional fields, valid schema values, dimension/measure formats, filter syntax, ordering, limits, and time dimension constraints. This transforms a generic parameter into a fully specified analytics query.

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 executes a Cube API query for advanced analytics, providing a specific verb ('execute') and resource ('Cube API query'). It distinguishes from most siblings by focusing on analytics queries rather than CRUD operations, though it doesn't explicitly differentiate from 'run_advanced_report' or 'create_advanced_report' which might be related.

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 implies usage context through the query structure documentation and mention of 'advanced analytics,' but doesn't explicitly state when to use this tool versus alternatives like 'run_advanced_report' or 'create_advanced_report.' It provides technical context but lacks explicit guidance on tool selection scenarios.

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