Query Variant Research Database

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable context: that data is from a local SQLite knowledge graph populated by boot seed and explicit writes, and that analysis tools do not write to it. This goes beyond the annotations, though it does not detail potential side effects (none expected).

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 concise, with three sentences front-loading the core purpose. Every sentence adds value: the first states the action, the second clarifies returns, and the third explains data source. No extraneous content.

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?

The tool is a read-only query with six filter parameters and an output schema. The description covers the essential behavioral traits (local, no writes) and data source. While it lacks details on pagination or sorting, the output schema likely covers return structure, making it adequately complete for its complexity.

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 includes descriptions for all six parameters, providing high coverage. According to guidelines, baseline is 3 when schema coverage is high. The description does not add any parameter-specific information beyond what's in the schema, so score remains 3.

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 'Search the local knowledge graph for stored variants', specifying the verb 'search', resource 'local knowledge graph', and scope 'stored variants'. It effectively distinguishes from sibling tools like classify_variant_acmg or query_protein_database by emphasizing the local and read-only nature.

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 mentions that no upstream API calls are made and data comes from a local SQLite knowledge graph, implying use when fast local queries are needed. However, it does not explicitly state when to use this tool versus alternatives like classify_variant_acmg or search_diseases, leaving usage guidance mostly implicit.

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