fetch_query

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

With no annotations, the description carries the burden for behavioral disclosure. It provides valuable context by stating that queries always run under the authenticated user's security privileges and focusing on read-style operations. However, it does not describe the response format, pagination, error behavior, or potential performance implications, leaving notable gaps.

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 front-loaded: purpose first, then concrete use cases, syntax examples, and a security note. Every sentence earns its place, and the example block is dense with necessary information for a custom query language.

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 custom-language tool, the description covers purpose, when to use, syntax, and security thoroughly. However, with no output schema, it does not specify the result shape, which is a moderate gap. Overall, it is complete enough for an agent to select and invoke the tool confidently.

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 schema covers the single query parameter at 100% baseline, but the description adds significant meaning through syntax examples and an explanation of what FETCH can do. This is essential for correctly constructing a query, going far beyond the schema's terse 'Must start with FETCH' requirement.

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 it executes a FETCH query against the ONE Framework instance and defines the query language's capabilities: retrieving, filtering, and aggregating data. This distinguishes it from sibling tools like list_entities and get_entity_record by emphasizing a custom, flexible query language.

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?

Explicitly lists use cases: explore data, discover relationships, run reports, and understand the data model. It does not explicitly name alternatives or exclusion conditions, but the purpose and syntax examples make it obvious when to use this tool versus the simpler sibling tools.

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