Omics AI MCP Server

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 optional filters and pagination but fails to cover critical aspects like authentication needs (implied by 'access_token' parameter), rate limits, error handling, or return format. For a query tool with 8 parameters, this leaves significant gaps in understanding how the tool behaves in practice.

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 front-loads the core purpose ('Query data from a table') and briefly mentions key features ('optional filters and pagination'). There is no wasted verbiage, making it easy to parse 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 (8 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain authentication requirements, result format, error conditions, or how to interpret parameters like 'filters' or 'order_by'. For a data query tool, this leaves too many operational questions unanswered.

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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by hinting at filters and pagination but doesn't provide additional context beyond what's in the schema (e.g., how filters work, pagination defaults). Baseline 3 is appropriate 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 verb ('Query') and resource ('data from a table'), specifying the core action. It distinguishes from siblings like 'count_rows' or 'get_schema_fields' by focusing on data retrieval rather than metadata or aggregation. However, it doesn't explicitly differentiate from 'sql_search', which might also query data, leaving some ambiguity.

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 like 'sql_search' or 'list_tables'. It mentions optional filters and pagination but doesn't specify use cases, prerequisites, or exclusions. This lack of context makes it challenging for an AI agent to choose between sibling tools effectively.

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